高职《数据结构》教学改革与探索

“数据结构”是计算机专业中一门重要的专业基础课程,同时又是计算机专业中一门难教和难学的课程。本文针对高职生源的实际情况,探索对该课程的教材、教学内容、教学方法、实验等方面进行改革,以提高该门课的教学效果。     

城市规划专题信息数据标准化研究

从现有城市规划数据的数据分类、数据代码、数据标准、数据报告入手,结合城市用地国家分类标准进行分析,提出一种包括专题规划信息分类、数据编码、数据分层的规划专题信息数据标准化技术。     

Artificial intelligence implication on energy sustainability in Internet of Things: A survey

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges.     

Algorithmic bias in data-driven innovation in the age of AI

Data-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, little is known about algorithmic biases that may present in the DDI process, and result in unjust, unfair, or prejudicial data product developments. Thus, this guest editorial aims to explore the sources of algorithmic biases across the DDI process using a systematic literature review, thematic analysis and a case study on the Robo-Debt scheme in Australia. The findings show that there are three major sources of algorithmic bias: data bias, method bias and societal bias. Theoretically, the findings of our study illuminate the role of the dynamic managerial capability to address various biases. Practically, we provide guidelines on addressing algorithmic biases focusing on data, method and managerial capabilities.     

Selecting an open innovation community as an alliance partner: Looking for healthy communities and ecosystems

Organizations build strategic alliances with other firms with the intent of tapping into partners’ resources and capturing long-term value from these relationships. Such partnerships are typically governed by contractual or equity arrangements with clear mutual obligations. More recently, however, organizations have begun to seek strategic partnerships with open innovation communities, which are novel digitally enabled forms of organizing, and where contractual commitments are not possible. Thus, selecting the right open innovation community as an alliance partner becomes a more complex decision. We follow how the organizational decision makers, in two technology firms that were pioneers of forming strategic alliances with open innovation communities, developed metrics around making such decisions. We build upon Shah and Swaminathan’s (2008) contingency model of alliance partner selection and consider how it applies to the case of partnering with open innovation communities. This framework was useful in to frame our findings, yet our work recognizes and builds upon two key differences: 1) the evaluation metrics used in selecting an open innovation community were more focused on value creation than value capture; and 2) open ecosystem considerations, and not just partner-specific metrics, featured prominently in this type of alliance partner evaluation. We develop the notions of community and ecosystem health to refer to these new metrics.     

大数据时代医学期刊数据共享方案的实施探讨

【目的】 介绍国外医学期刊研究数据共享经验和《中华医学杂志英文版》实践,探讨适合我国期刊的数据共享方案。【方法】 对相关文献进行梳理,分析国外医学期刊有关数据共享的政策与要求,以及国内外医学期刊数据共享实施中存在的问题。【结果】 国外医学期刊虽然致力于推进数据共享,但总体效果不尽人意;我国期刊对数据共享认识不足,重视程度不够,在数据共享政策制定方面进展缓慢。期刊需要多方合作,采取相应措施,积极开展数据共享工作。【结论】 我国期刊数据共享工作尚处于初级阶段。期刊可结合自身情况,制定和完善数据共享方案。     

我国学术期刊开放获取政策现状及制定策略研究

【目的】 为我国学术期刊开放获取(Open Access,OA)政策的制定提供建议。【方法】 使用网络调查法和案例分析法,调研国内外学术期刊OA政策现状,分析国内政策的不足及国外政策的经验。【结果】 国内学术期刊OA政策存在政策不健全、政策分散、政策简单等问题;国外学术期刊在版权许可、质量控制、费用、自存储等方面已构建较完备的OA政策体系。【结论】 从建立合作参与的政策模式、完善的政策体系和评估与修订机制三个方面为制定我国学术期刊OA政策提出建议。     

COVID-19, digital privacy,and the social limits on data-focused public health responses

The implementation of digital contact tracing applications around the world to help reduce the spread of the COVID-19 pandemic represents one of the most ambitious uses of massive-scale citizen data ever attempted. There is major divergence among nations, however, between a “privacy-first” approach which protects citizens’ data at the cost of extremely limited access for public health authorities and researchers, and a “data-first” approach which stores large amounts of data which, while of immeasurable value to epidemiologists and other researchers, may significantly intrude upon citizens’ privacy. The lack of a consensus on privacy protection in the contact tracing process creates risks of non-compliance or deliberate obfuscation from citizens who fear revealing private aspects of their lives – a factor greatly exacerbated by recent major scandals over online privacy and the illicit use of citizens’ digital information, which have heightened public consciousness of these issues and created significant new challenges for any collection of large-scale public data. While digital contact tracing for COVID-19 remains in its infancy, the lack of consensus around best practices for its implementation and for reassuring citizens of the protection of their privacy may already have impeded its capacity to contribute to the pandemic response.     

地学中海量数据的并行可视化研究进展

地球科学中观测和数值模拟结果数据爆炸式的增长,给可视化和分析这些海量数据带来了极大的挑战,将海量数据并行可视化技术应用到地球科学中是解决这一问题的有效手段。地球科学中海量数据可视化是目前国际科学可视化研究的一个新方向。本文首先介绍海量数据并行可视化的几个步骤,然后总结了地球科学中海量数据的特点,重点讨论了海量数据并行可视化在地幔对流、地震波传播以及海啸数值模拟等可视化研究中的应用实例。     

基于DECE算法的蓝牙通讯加密

本文在分析蓝牙通讯加密技术缺陷的基础上,提出了DECE通讯加密算法,使得蓝牙对于数据通讯的保密性得到了强化。该算法也可用于网络中的嵌入式设备。