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关键词： 首发经济   智慧城市   黑神话:悟空   技术+文化”模式  

摘要： 首发经济凭借引入前沿产品与服务、激发消费活力、汇聚创新资源等特质,为智慧城市在经济、科技、社会等多维度的建设与发展提供有力支撑。该文以现象级游戏《黑神话:悟空》的首发为范例,探究首发经济在智慧城市发展进程中的独特价值及创新驱动效能。通过分析该游戏“技术+文化”的新产品、新服务与新营销模式及其所衍生的产业新生态模式,发现其在智慧城市建设中的智慧基建、文化旅游推广以及产业优化升级维度具有促进作用。然而,该首发模式在促进智慧城市建设中也存在诸如引入期增长与产业活力延续性、技术文化融合以及利益相关者定位等问题。鉴于此,该文基于“谋新求快”、可持续性、均衡性的基本原则,提出加强首发产品过渡期的协同创新效应、以新质生产力平衡首发产品商业化与智慧城市建设、以韧性城市理念与游戏文化IP打造可持续城市新生态的创新策略,为提升技术自觉与文化自信以及未来智慧城市的高质量发展提供新的视角和实践方向。

关键词： 新工科   计算机科学与技术   创新型人才培养  

摘要： 本文探讨了新工科背景下计算机科学与技术专业创新型人才培养模式。文章首先分析了传统教学模式存在的课程内容滞后、跨学科培养碎片化、实践平台不足等问题,然后提出了一套以“产教融合、学科交叉、能力导向”为核心的人才培养模式,包括教学内容更新、学科融合、强化实践环节、提升创新能力以及对接社会需求五个方面,旨在培养具备跨学科思维、创新能力和实践能力的复合型人才。文章针对当前计算机科学与技术专业人才培养与新工科发展需求之间的矛盾,提出了较为全面的改革方案,对于提高人才培养质量、促进学科发展具有重要的现实意义。文章的研究成果可以为其他高校相关专业的教学改革提供参考。This article explores the cultivation mode of innovative talents for the major of Computer Science and Technology under the background of emerging engineering education. Firstly, the article analyzes the problems existing in the traditional teaching mode, such as the lagging curriculum content, fragmented interdisciplinary cultivation, and insufficient practical platforms. Then, it proposes a set of talent cultivation mode with the core of “integration of industry and education, interdisciplinary integration, and ability orientation”, which includes five aspects: updating teaching content, integrating disciplines, strengthening practical links, enhancing innovation ability, and aligning with social needs. The aim is to cultivate compound talents with interdisciplinary thinking, innovation ability, and practical ability. Aiming at the contradiction between the current talent cultivation for the major of Computer Science and Technology and the development needs of emerging engineering education, this article puts forward a relatively comprehensive reform plan, which has important practical significance for improving the quality of talent cultivation and promoting the development of disciplines. The research results of this article can provide references for the teaching reforms of related majors in other universities.

关键词： Human action recognition   Machine operation analysis   Artificial intelligence   Deep learning   Wavelet transformation   Convolutional neural network (CNN)   Long short-term memory (LSTM)   Industrial machinery   Video-based sensing   Operation process analysis   Value stream mapping  

摘要： Automating work process analysis is crucial in manufacturing to improve efficiency and productivity. However, traditional deep learning methods often fail to capture subtle temporal changes in machine operations, such as varying speeds. We propose a cost-effective approach called pseudo-sensing, which simulates sensor data by measuring machine speeds directly from video using wavelet transformation, a mathematical tool for time-frequency analysis. This approach eliminates the need for physical sensors. We evaluated pseudo-sensing by integrating it into two task classification models. The first is a convolutional neural network-long short-term memory (CNN-LSTM) model, which extracts spatial features via a CNN and learns temporal patterns using an LSTM. The second is a three-dimensional residual network (3D ResNet, R3D), designed to process spatiotemporal data simultaneously. With pseudo-sensing, the CNN-LSTM's micro-F1 score-an accuracy metric averaging precision and recall across all classes-improved from 0.712 to 0.736 (+2.4 points), while R3D's score rose from 0.675 to 0.701 (+2.7 points). To assess general applicability, we tested pseudo-sensing on another dataset featuring diverse machine motions: unidirectional movements (e.g., conveyor belts), oscillatory movements (e.g., pendulum-like motions), rotational movements (e.g., rotary presses), and intermittent movements (e.g., blinking or toggling mechanisms). The method achieved an 83% success rate in identifying machine dynamics. By leveraging deep learning, this method integrates video-based machine operation sensing with task recognition, considering both human actions and machine states. Eliminating additional sensors while enhancing accuracy and efficiency, pseudo-sensing offers broad potential for advancing manufacturing process analysis.

关键词： 人工智能   计算机专业   教学改革   实践探索  

摘要： 人工智能是引领新一轮科技革命和产业变革的战略性技术,对计算机专业人才培养提出了新要求。高校计算机专业要主动适应人工智能发展需求,加快人工智能教学改革步伐。本文分析了人工智能教学在计算机专业中开展的必要性,指出一些课程体系滞后、教学模式较为单一、资源相对匮乏、师资相对不足等不足,从教学内容优化、课程体系构建、资源平台建设、师资队伍提升等方面提出相关对策,对新形势下深化计算机专业人工智能教学改革具有参考价值。

关键词： 个性化教学   计算思维   数据结构  

摘要： 个性化教学是当前教育学界的研究热点,在线教育平台积累的大量数据为个性化教学研究提供了丰富的数据支持。本文以“数据结构”课程为例,探索了个性化教学在计算机专业教育中的应用,并提出了一种个性化教学方法,即通过在线教学平台建立知识点、学习资源和习题间的关联,教师对学生在平台的学习数据进行分析,并依据学习数据及这些关联关系针对学生个体进行深度精准的个性化教学设计及个性化推荐。实践表明,该方法能显著提高中等水平学生的计算思维和编程能力。

关键词： Entropy   Vehicle dynamics   Games   Electronic mail   Decision making   Collaboration   Transformers   Training   Faces   Computer science   Cooperative reinforcement learning   multi-agent transformer   soft actor-critic  

摘要： Effective multi-intersection collaboration is crucial for mitigating urban traffic congestion through reinforcement learning (RL)-based traffic signal control (TSC). Existing work mainly considers scenarios involving a single vehicle type, where cooperation is typically limited to neighboring intersections. However, in urban traffic scenarios where high priority vehicles coexist with ordinary vehicles, considering only a limited number of neighboring nodes may be insufficient to ensure the swift passage of high priority vehicles while minimizing the impact on overall traffic efficiency. Therefore, we formulate the multiple intersections' decision-making process in urban scenarios as a Markov game and propose a novel centralized cooperative RL framework called MATLIT to solve the game. Specifically, we adopt a multi-agent transformer (MAT)-based architecture that facilitates efficient global cooperation among intersections. The attention mechanism and auto-regressive process of the MAT effectively mitigate the curse of the dimensionality problem, which guarantees MATLIT to tackle large-scale traffic scenarios. Meanwhile, the stability and sequence action generation capacity of the MAT-based architecture is further enhanced by incorporating MAT with a gated mechanism. Furthermore, considering the inherent topological constraints in urban traffic scenarios, we utilize graph attention networks (GATs) to capture graph-structured mutual influences. Additionally, in response to the urban traffic scenarios with various types of high priority vehicles that have time-varying priorities, we integrate the soft actor-critic (SAC) algorithm to enhance the exploration capabilities of our framework, allowing it to learn robust strategies in heterogeneous traffic conditions. Extensive experiments demonstrate that our proposed MATLIT framework outperforms all baselines and can reduce high priority vehicles' waiting time by 24.57% while reducing the average waiting time of all vehicle

关键词： Fraud   Credit cards   Logic gates   Data models   Predictive models   Graph neural networks   Feature extraction   Finance   Computer science   Training   Fraud detection   semi-supervised learning   graph neural network  

摘要： Credit card fraud is a severe issue that causes significant losses for both cardholders and issuing banks. Existing methods utilize machine learning-based classifiers to identify fraudulent transactions from labeled transaction records. However, labeled data are often scarce compared to the billions of real transactions due to the high cost of annotation, which means that previous methods do not fully utilize the rich features of unlabeled data. What's more, contemporary methods succumb to a fallacy of unawareness of the local risk structure and the inability to capture certain risk patterns. Therefore, we propose the Risk-aware Gated Temporal Attention Network (RGTAN) for fraud detection in this work. Specifically, we first build a temporal transaction graph based on the transaction records, which consists of temporal transactions (nodes) and their interactions (edges). Then we leverage a Gated Temporal Graph Attention (GTGA) Mechanism to propagate messages among the nodes and learn adaptive representations of transactions. We also model the fraud patterns through risk propagation, taking advantage of the relations among transactions. More importantly, we devise a neighbor risk-aware representation learning layer to enhance our method's perception of multi-hop risk structures. We conduct extensive experiments on a real-world credit card transaction dataset and two public fraud detection datasets. The results show that our proposed method, RGTAN, outperforms other state-of-the-art methods on three fraud detection datasets. The risk-aware semi-supervised experiments also demonstrate the excellent performance of our model with only a small fraction of manually labeled data. Moreover, RGTAN has been deployed in a world-leading credit card issuer for credit card fraud detection, and the case study results show the effectiveness of our method in uncovering real-world fraud patterns.

关键词： Lower bound   Heuristic algorithms   Data mining   Computer science   Euclidean distance   Electronic mail   Training   Time measurement   Standards   Costs   Time series analysis   data mining   dynamic time warping  

摘要： Motif discovery is a critical operation for analyzing series data in many applications. Recent works demonstrate the importance of finding motifs with Dynamic Time Warping. However, existing algorithms spend most of their time in computing lower bounds of Dynamic Time Warping to filter out the unpromising candidates. Specifically, the time complexity for computing these lower bounds is O(L) for each pair of subsequences, where L is the length of the motif (subsequences). This paper proposes two new lower bounds, called LBf and LBM, both of them only cost amortized O(1) time for each pair of subsequences. On real datasets, the proposed lower bounds are at least one magnitude faster than the state-of-the-art lower bounds used in motif discovery while still keeping satisfying effectiveness. Based on these faster lower bounds, this paper designs an efficient motif discovery algorithm that significantly reduces the cost of lower bounds. The experiments conducted on real datasets show the proposed algorithm is 5.6 times faster than the state-of-the-art algorithms on average.