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关键词： Computer science and technology   Big data   Current state of development  

摘要： Computer science and technology play an important role in people’s lives and work,especially in the era of big *** application of computer science and technology is conducive to improving people’s production and living standards and provides a strong impetus for China’s social and economic ***,this paper analyzes the application of computer science and technology in the context of big data and puts forward new trends and requirements for its future development,aiming to provide some references for the computer science and technology industry.

关键词： 黑神话:悟空   3A游戏   网络游戏   中华优秀传统文化   文化辨识度   文化标识  

摘要： 该文以我国首个3A级网络游戏《黑神话:悟空》为例,探讨以网络游戏提升中华文化影响力的路径。笔者基于对该游戏的玩家体验,主要采用文献分析方法进行研究。该文包含三部分:第一部分介绍游戏基本情况及其全球普及情况,指出网络游戏为中华文化的全球传播开辟了新路径;第二部分从把握市场、技术创新和文化底蕴三个方面分析目标游戏强势出圈的原因;第三部分针对性提出用网络游戏提升中华文化影响力的对策。该文认为游戏开发者首先要充分挖掘中华优秀传统文化资源,提升网络游戏的文化辨识度;其次在设计游戏时要注重价值引领,打造国际认可的中华文化标识;最后要把握文化市场规律,尊重用户体验,让网络游戏成为提升中华文化全球影响力的有效载体。

关键词： 高等教育   互联网   教师职业道德及专业能力  

摘要： 随着科技的发展,“互联网+”已经融入到生活的方方面面,“互联网 + 教育”同样蓬勃发展,新时代对教育及教师提出了新的要求。因此,文章针对“互联网+”背景下新时代高校教师职业道德规范及专业能力问题,首先,介绍了“互联网 + 教育”的含义;然后,分析了面临的机遇与挑战;最后,提出了规范高校教师职业道德和提升专业能力的对策。同时,文章结合专业领域,以计算机专业教师为例进行分析及研究。With the advancement of technology, the “Internet+” concept has permeated every aspect of life, and “Internet + Education” is flourishing accordingly. The new era has set forth new demands for education and educators. Consequently, this paper addresses the issues of professional ethics and professional capacity enhancement for university teachers under the “Internet+” backdrop. Initially, the paper elucidates the meaning of “Internet + Education.” Subsequently, it analyzes the opportunities and challenges faced. Finally, it proposes strategies for regulating the professional ethics of university teachers and enhancing their professional capabilities. Moreover, the paper integrates a professional field perspective, focusing on computer science educators for analysis and research.

关键词： artificial intelligence   big data   quantum computing   Industry 4.0   Internet of Things   machine learning   robotics   network security   technological innovation   societal transformation  

摘要： This editorial article introduces the Special Issue entitled "Advanced Technologies and Applications in Computer Science and Engineering", which highlights a diverse range of contemporary research and technological advancements in these fields. The issue features selected contributions that explore cutting-edge innovations, including artificial intelligence, machine learning, quantum computing, the Internet of Things, and big data analytics, emphasizing their transformative impact on industry, science, and society. The articles address both theoretical advancements and practical applications, showcasing novel approaches to solving complex computational and engineering problems. Special attention is given to the challenges associated with the integration and implementation of these emerging technologies, including scalability, security, and ethical considerations. The selected studies present innovative solutions in key areas such as robotics, network security, cloud computing, and data-driven decision-making, contributing to the development of efficient, intelligent, and sustainable technological solutions. By bringing together a multidisciplinary perspective, this Special Issue aims to foster collaboration among researchers, engineers, and industry professionals, ultimately advancing the frontiers of computer science and engineering.

关键词： Sensors   Autonomous aerial vehicles   Approximation algorithms   Disasters   Wireless sensor networks   Capacitive sensors   Water pollution   Social networking (online)   Contamination   Computer science   UAV deployment   sensor placement   submodular function maximization   approximation algorithms  

摘要： In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K , the problem is to find a subset S with K nodes from a graph G , so that a given submodular function f(S) on S is maximized and the induced subgraph G[S] by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in S , the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel 1-1/e/2h+2 -approximation algorithm for the problem, improving the best approximation ratio 1-1/e/2h+3 for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where e is the base of the natural logarithm, h is a parameter that depends on the problem and its typical value is 2. In addition, when h=2 , the algorithm approximation ratio is at least 1-1/e/5 and may be as large as 1 in some special cases when K <= 23 , and is no less than 1-1/e/6 when K >= 24 , compared with the current best approximation ratio 1-1/e/7(=1-1/e/2h+3) for the problem. Finally, experimental results in the application of deploying a UAV network demonstrate that, the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and the throughput of the deployed UAV network by the proposed algorithm is up to 9.7% larger than those by the algorithms. Furthermore, the empirical approximation ratio of the proposed algorithm is between 0.7 and 0.99, which is close to the theoretical maximum value one.

关键词： Streams   Windows   Out of order   Binary trees   Throughput   Aggregates   Partitioning algorithms   Time complexity   Indexes   Computer science   Bulk eviction   incremental computation   out-of-order data streams   sliding window aggregation   stream algorithm  

摘要： Sliding window aggregation, which extracts summaries from data streams, is a core operation in streaming analysis. Though existing sliding window algorithms that perform single eviction and insertion operations can achieve a worst-case time complexity of O(1) for in-order streams, real-world data streams often involve out-of-order data and exhibit burst data characteristics, which pose performance challenges to these sliding window algorithms. To address this challenging issue, we propose Gecko - a novel sliding window aggregation algorithm that supports bulk eviction. Gecko leverages a granular-based eviction strategy for various bulk sizes, enabling efficient bulk eviction while maintaining the performance close to that of in-order stream algorithms for single evictions. For large data bulks, Gecko performs coarse-grained eviction at the chunk level, followed by fine-grained eviction using leftward binary tree aggregation (LTA) as a complementary method. Moreover, Gecko partitions data based on chunks to prevent the impacts of out-of-order data on other chunks, thereby enabling efficient handling of out-of-order data streams. We conduct extensive experiments to evaluate the performance of Gecko. Experimental results demonstrate that Gecko exhibits superior performance over other solutions, which is consistent with theoretical expectations. In real-world data scenarios, Gecko improves the average throughput of the state-of-the-art algorithm b_FiBA by 1.7 times, with a maximum improvement of up to 3.5 times. Gecko also demonstrates the best latency performance among all compared schemes.

关键词： Consumer electronics   Internet of Things   Security   Computational modeling   Blockchains   Accuracy   Logic gates   Computer science   Training   Semantic Web   Artificial intelligence   block chain   Komodo Mlipir optimization   multi-threat detection   Web 3.0  

摘要： Consumer Electronic Devices have become an open network model because of the infusion of the Internet of Things (IoT) and other communication technologies such as 5G/6G. Though these devices have provided the high-end sophistication even to common person, but it has proved its darker side by triggering more security breaches and privacy problems. Hence, securing and authenticating these Internet enabled consumer devices has become a probable issue to be solved for safer and secured communication. Therefore, this paper presents a novel fusion of Web 3.0- based Blockchain (WBC) and Deep learning (DL) technique for securing consumer electronic devices in an IoT ecosystem. The proposed framework k(MTD-BCAM) is devised into two components: Multiple-Threat Detection(MTD) and Access Management Mechanism(AMM). In the first component, a DL model is applied for threat detection, whereas WBC is meant for an efficient authentication process. Furthermore, a novel residual fast-gated recurrent neural network is proposed. To reduce the complexity, the komodo Mlipir optimization (KMO) approach is used to tune the hyper-parameters of the network. The comprehensive experimental outcome study of the proposed approach employs NSL-KDD datasets in which the distinct metrics of both DL and Blockchain (BC) are measured and analyzed. Results demonstrated the superior accuracy of the model by achieving 99.78% with less computational time and higher transaction speed. Additionally, the statistical validation and security strength of the model are also analyzed and examined with the varied state-of-art models.

关键词： Pedestrians   Surveys   Trajectory   Target tracking   Cameras   Reviews   Object tracking   Circuits and systems   Computer science   Computational modeling   Multiple object tracking   multiple pedestrian tracking   occlusion handling   trajectory prediction   trajectory recovery  

摘要： As an intermediate task in computer vision, multiple pedestrian tracking (MPT) aiming at tracking the pedestrians from a given video, has attracted attention due to its potential academic and commercial value. However, pedestrians commonly suffer from occlusion due to diverse and complex scenarios, which increases the challenge of this task. This survey provides comprehensive review in terms of occlusion scenarios encountered during MPT, and investigates the model robustness of the existing methods in this scenarios. Firstly, this survey introduces the various and states of occlusion. Secondly, the related occlusion datasets are introduced. Subsequently, we categorize existing occlusion handling methods according to the tracking process and detail their pros and cons. In addition, occlusion handling precision (OHP) metric is proposed to evaluate the ability of a tracker in handling occlusion in this survey. Moreover, comprehensive analyzes and discussions in several public datasets are provided to verify the effectiveness of these methods. Finally, the existing issues and future directions for occlusion handling methods are discussed. In doing so, this work serves as a foundation for future research by providing researchers with information about the occlusion handling method of MPT.

关键词： Buildings   Optimization   Collaboration   Global communication   Cultural differences   Videos   Mathematical models   History   Computer science   Chaos   Collaborative recommendation   environment-classes   agents   roles   groups   and objects (E-CARGO)   national image resource   targeted international communication  

摘要： With the acceleration of globalization, the targeted international communication of national images contributes to enhancing a nation's soft power and international recognition. It is challenging to select appropriate resources from the mass candidates for creating promotional works of national image. Existing research only focuses on the methodologies and lacks the systematic modeling and solving of national image resources recommendation. A collaborative recommendation approach to national image resources is proposed for targeted international communication. In it, the multidimensional features of national image resources and characteristics of communication audiences are modeled, and an evaluation mechanism is proposed to measure the comprehensive compatibility between national image resources and communication audiences. By innovatively introducing the role-based collaboration (RBC) theory and the environment-classes, agents, roles, groups, and objects (E-CARGO) model, the national image resources recommendation is formalized as a collaborative optimization problem. The mathematical model is built and solved via an optimization package. Finally, the case study and experiments show that the approach is efficient, feasible, and conducive to enhancing the efficiency of selecting national image resources. It offers a novel research paradigm for targeted international communication.

关键词： Testing   Complexity theory   Probability distribution   Computer science   Upper bound   Surveys   Polynomials   Machine learning algorithms   Machine learning   Limiting   Distribution testing   tolerant testing   non-tolerant testing   sample complexity  

摘要： The framework of distribution testing is currently ubiquitous in the field of property testing. In this model, the input is a probability distribution accessible via independently drawn samples from an oracle. The testing task is to distinguish a distribution that satisfies some property from a distribution that is far in some distance measure from satisfying it. The task of tolerant testing imposes a further restriction, that distributions close to satisfying the property are also accepted. This work focuses on the connection between the sample complexities of non-tolerant testing of distributions and their tolerant testing counterparts. When limiting our scope to label-invariant (symmetric) properties of distributions, we prove that the gap is at most quadratic, ignoring poly-logarithmic factors. Conversely, the property of being the uniform distribution is indeed known to have an almost-quadratic gap. When moving to general, not necessarily label-invariant properties, the situation is more complicated, and we show some partial results. We show that if a property requires the distributions to be non-concentrated, that is, the probability mass of the distribution is sufficiently spread out, then it cannot be non-tolerantly tested with o(root n) many samples, where n denotes the universe size. Clearly, this implies at most a quadratic gap, because a distribution can be learned (and hence tolerantly tested against any property) using O(n) many samples. Being non-concentrated is a strong requirement on properties, as we also prove a close to linear lower bound against their tolerant tests. Apart from the case where the distribution is non-concentrated, we also show if an input distribution is very concentrated, in the sense that it is mostly supported on a subset of size s of the universe, then it can be learned using only O(s) many samples. The learning procedure adapts to the input, and works without knowing s in advance.