Gender-specific patterns in the artificial intelligence scientific ecosystem

Gender disparity in science is one of the most focused debating points among authorities and the scientific community. Over the last few decades, numerous initiatives have endeavored to accelerate gender equity in academia and research society. However, despite the ongoing efforts, gaps persist across the world, and more measures need to be taken. Using social network analysis, natural language processing, and machine learning, in this study, we comprehensively analyzed gender-specific patterns in the highly interdisciplinary and evolving field of artificial intelligence for the period of 2000–2019. Our findings suggest an overall increasing rate of mixed-gender collaborations. From the observed gender-specific collaborative patterns, the existence of disciplinary homophily at both dyadic and team levels is confirmed. However, a higher preference was observed for female researchers to form homophilous collaborative links. Our core-periphery analysis indicated a significant positive association between having diverse collaboration and scientific performance and experience. We found evidence in support of expecting the rise of new female superstar researchers in the artificial intelligence field.     

Governance of artificial intelligence: A risk and guideline-based integrative framework

This study addresses the growing challenge of governing artificial intelligence (AI) arising from the risks that it increasingly poses to the public sector and society. Based on an in-depth literature analysis, we first identify AI risks and guidelines and classify them into six categories, including technological, data, and analytical risks and guidelines, informational and communicational risks and guidelines, economic risks and guidelines, social risks and guidelines, ethical risks and guidelines, as well as legal and regulatory risks and guidelines. These risks and guidelines are then elaborated and transferred into a four-layered conceptual framework for AI governance. The framework interrelates AI risks and AI guidelines by means of a risk management and guidance process, resulting in an AI governance layer depicting the process for implementation of customised risk mitigation guidelines. The framework constitutes a comprehensive reference point for developing and implementing AI governance strategies and measures in the public sector.     

Perceptions of artificial intelligence: A survey of academic librarians in Canada and the United States

This study aims to evaluate the perceptions of librarians with regard to artificial intelligence in academic libraries. An online survey of 24 questions was distributed through library distribution lists in Canada and the United States at the end of the summer in 2019. Findings suggest that librarians do not agree on a definition of artificial intelligence which is in keeping with this emerging field. The survey responses highlight the fact that academic librarians require more training with regard to artificial intelligence and its potential applications in libraries. Other important implications include a recognition that library patrons are interested in AI and that little to no programming about it has been offered in academic libraries. Very few studies have focused on academic librarians' perceptions of AI. This article highlights some useful practical implications for AI technologies in libraries and how AI could help improve library services and workflows.     

人工智能在档案资源开发利用中的应用分析

人工智能的应用为档案资源开发利用带来机遇,但目前在档案领域的应用尚处于探索时期。一方面,智能语音技术、光学字符识别技术、人脸识别技术等能够促进档案资源的文本化、数字化与检索智能化;另一方面,档案资源类型复杂、指导理念与技术脱节、用户信息泄露、算法伦理等问题制约着其发挥作用。本文通过分析人工智能在档案资源开发利用中的现状,探讨其在资源层面、理念层面与技术层面遇到的困境,提出通过技术聚焦、理念更新、用户信息加密等策略全面优化人工智能在档案资源开发利用中的应用,为充分挖掘与实现档案价值赋能。     

新世纪国际人工智能研究领域可视化分析

目的：全面了解新世纪国际人工智能领域的研究现状与研究热点。方法：运用TDA软件,利用文献统计分析、关键词共现分析的方法揭示研究热点。结果：国际人工智能领域文献量呈上升趋势,其中美国发文量排名第一,中国位居第六。人工智能的主研究领域包括计算机科学、工程学、自动化与控制系统3个学科。研究热点为遗传算法、神经网络和机器学习。结论：新世纪国际人工智能研究涉及多个学科,研究热点集中在知识获取、知识表示和问题求解3个宏观层面,神经网络、遗传算法和机器学习是研究者们关注的核心。     

人工智能背景下科技论文摘要的机器翻译与译后编辑

神经网络机器翻译的发展为作者英译科技论文摘要提供了便利,也丰富了科技期刊英文编辑的工作内涵。通过借鉴(GB/T 40036—2021)《翻译服务 机器翻译结果的译后编辑要求》中对机器翻译译后编辑类型、任务及目标等相关问题的界定来探讨机译英文摘要的编辑加工方法不失为一次全新而有益的尝试。本文辨析了作者、编辑进行机译英文摘要译后编辑的不同类型,阐述了编辑层面的译后编辑任务分解流程,归纳了机译英文摘要的常见错误类型,探讨了与摘要内容相适应的译后编辑策略,并认为编辑应坚守人机互动翻译模式中的主导地位。     

美丽的月亮源自心中的太阳——访著名诗人、歌词作家石祥

诗的风格和意境像诗人的心性,有的清新隽永,有的忧伤缠绵,有的千般柔情,有的豪情万丈.而石祥的诗既富"日月星"的浪漫,更有"兵之歌"的壮美.     

Barriers to artificial intelligence adoption in smart cities: A systematic literature review and research agenda

Artificial intelligence (AI) plays a prominent role in smart cities' development and offers benefits to different services such as finance, healthcare, security, agriculture, transport, education, and manufacturing. Despite the expected benefits, the adoption of AI varies from one smart city to another, due in part to barriers that can inhibit a smart city from adopting AI. The aim of this paper is to provide a comprehensive view of the barriers faced by smart cities. Through a systematic literature review, this study identifies 18 primary and secondary barriers grouped into three main categories — technology, environment, and organization. This research contributes to the literature by developing a typology of AI adoption barriers based on the Technology-Organization-Environment (TOE) perspective. The typology provides a novel mapping of the barriers to AI adoption faced by smart cities and suggests directions for further investigation through a cohesive research agenda. At a practical level, the findings will allow policymakers, planners, and citizens to make more informed decisions about AI adoption. Practical implications are also proposed for guiding smart cities to increase the adoption of AI.     

Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda

To lay the foundation for the special issue that this research article introduces, we present 1) a systematic review of existing literature on the implications of the use of Artificial Intelligence (AI) in public governance and 2) develop a research agenda. First, an assessment based on 26 articles on this topic reveals much exploratory, conceptual, qualitative, and practice-driven research in studies reflecting the increasing complexities of using AI in government – and the resulting implications, opportunities, and risks thereof for public governance. Second, based on both the literature review and the analysis of articles included in this special issue, we propose a research agenda comprising eight process-related recommendations and seven content-related recommendations. Process-wise, future research on the implications of the use of AI for public governance should move towards more public sector-focused, empirical, multidisciplinary, and explanatory research while focusing more on specific forms of AI rather than AI in general. Content-wise, our research agenda calls for the development of solid, multidisciplinary, theoretical foundations for the use of AI for public governance, as well as investigations of effective implementation, engagement, and communication plans for government strategies on AI use in the public sector. Finally, the research agenda calls for research into managing the risks of AI use in the public sector, governance modes possible for AI use in the public sector, performance and impact measurement of AI use in government, and impact evaluation of scaling-up AI usage in the public sector.     

Signatures of capacity development through research collaborations in artificial intelligence and machine learning

Extant studies suggest that the proximity between the researchers and their structural positioning in the collaboration network may influence productivity and performance in collaboration research. In this paper, we analyze the co-authorship networks of the three countries, viz. the USA, China, and India, constructed in consecutive non-overlapping 5-year long time windows from bibliometric data of research papers published in the past decade in the rapidly evolving area of Artificial Intelligence and Machine Learning (AI&ML). Our analysis relies on the observations ensued from a comparison of the statistical properties of the evolving networks. We consider macro-level network properties which describe the global characteristics, such as degree distribution, assortativity, and large-scale cohesion etc., as well as micro-level properties associated with the actors who have assumed central positions, defining a core in the network assembly with respect to closeness centrality measure. For the analysis of the core actors, who are well connected with a large number of other actors, we consider share of their affiliations with domestic institutes. We find dominant representation of domestic affiliations of the core actors for high productivity cases, such as China in the second time window and the USA in the first and second both. Our study, therefore, suggests that the domestic affiliation of the core actors, who could access network resources more efficiently than other actors, influences and catalyzes the collaborative research.     

From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation

Artificial intelligence has become an important tool for governments around the world. However, it is not clear to what extent artificial intelligence can improve decision-making, and some policy domains have not been the focus of most recent studies, including the public budget process. More specifically, budget allocation is one of the areas in which AI may have greatest potential. Therefore, this study attempts to contribute to this gap in our existing knowledge by answering the following research question: To what extent can artificial intelligence techniques help distribute public spending to increase GDP, decrease inflation and reduce the Gini index? In order to respond to this question, this article proposes an algorithmic approach on how budget inputs (specific expenditures) are processed to generate certain outputs (economic, political, and social outcomes). The authors use the multilayer perceptron and a multiobjective genetic algorithm to analyze World Bank Open Data from 1960 to 2019, including 217 countries. The advantages of implementing this type of decision support system in public expenditures allocation arise from the ability to process large amounts of data and to find patterns that are not easy to detect, which include multiple non-linear relationships. Some technical aspects of the expenditure allocation process could be improved with the help of these kinds of techniques. In addition, the results of the AI-based approach are consistent with the findings of the scientific literature on public budgets, using traditional statistical techniques.     

情报学“竞争”话语权的旁落、回归与超越

认为情报是人类社会竞争性活动的产物,是提升组织与个体竞争力的关键因素。探讨情报学“竞争”话语权经历了“否定之否定”的演变过程,即情报学发展从重视竞争到淡化竞争再到超越竞争。分析《基于博弈论的国家竞争力评价体系研究》既是一部回归“竞争”的情报学专著,又是一部超越“竞争”的跨学科力作。     

Creating a smart nation: Strategy,policy, intelligence,and information

In an age characterized by distributed information, where the majority of the expertise is in the private sector, the concept of “central intelligence” is an oxymoron. The greatest threat to both national security and national economic competitiveness is ignorance—uninformed decision making. Intelligence communities are slowly discovering that they should not send a spy where a schoolchild can go, and that spies are not harnessing the vast distributed intelligence of the private sector. Unfortunately, the culture of intelligence in most countries believes that its uniqueness rests on secrets rather than thinking—on producing secrets rather than informing policy. To survive in the 21st century, every nation must become a “smart nation” and engage all of its citizens—every citizen must be a collector, producer, and consumer of intelligence—and, thus, create the Virtual Intelligence Community. To integrate and make the best use of both open-source intelligence and traditional classified intelligence, each nation must establish a National Information Strategy which addresses connectivity, content, coordination, and computational security.     

Fighting terrorism,making war: Critical insights in the management of information and intelligence

This article reviews and analyzes the essential lessons of information and intelligence management lessons from recent terrorist attacks and the decision to attack Iraq. It draws on evidence from the 9/11 Commission, the Senate investigation of intelligence and the war in Iraq, the Butler Commission in Britain, and other sources. It identifies and explores several themes, including the following: relationship between intelligence work and other information work; inconclusive nature of intelligence; role of executive leadership in successful intelligence work, including the leader's need for information and decision-making style; role of organizational culture and human factors; the need for sound information management practices and systems; and the need to evaluate information for decision making and other actions. The article concludes with a discussion of strategic information management policies and approaches that are essential to improve the nation's information and intelligence management capacity.     

The rise of the machines: Artificial intelligence meets scholarly content

Key points

• New forms of human/machine dialogue are emerging as robots understand vast amounts of content rather than simply indexing content as strings of characters.
• Recognizing strings of characters as entities (e.g. = names = authors) allows for meaningful associations between entities and reasoning over these relationships.
• Web‐scale adoption of the Semantic Web approach has been slow because it is too complex to implement and does not scale.
• User intent, discovered through conversational models of human–computer interaction, allows for a deeper understanding of exactly what researchers are looking for.
• Personal agents hold the promise of finding information that we will find useful before we have started to look for it.
• Publishers can use Academic Knowledge APIs to interpret academic user queries and find rich information from the Microsoft Academic Graph.

     

“复关”后经济情报的保密工作和对策

“复关”后经济情报的保密工作和对策滕代弟，靖继鹏Ａｂｓｔｒａｃｔ：Ｔｈｅｗｏｒｋｏｆｋｅｅｐｉｎｇｔｈｅｅｃｏｎｏｍｉｃｉｎｔｅｌｌｉｇｅｎｃｅｉｎｓｅｃｒｅｃｙｍｕｓｔｂｅｄｏｎｅｗｅｌｌａｆｔｅｒｔ？ｈｌｎａ＇ｓｒｅｅｎｔｒｙｉｎｔｏｔｈｅＧｅｎ...     

企业竞争情报安全人机因素分析

从人-机系统的观点分析影响企业竞争情报安全的因素,把影响企业竞争情报安全的主因素分为人、机和环境三个方面;再次基础上进行子因素的划分认为影响企业竞争情报安全的子因素主要包括：人的意识、人的行为、人际网络、信息技术、内部和外部环境等;详细分析每一种因素对企业竞争情报安全的影响,并给出一些行之有效的安全措施。     

网络营销活动中竞争情报需求分析

网络营销是信息经济时代企业重要的营销手段,本文从情报的角度剖析网络营销,分析了网络营销活动的构成要素,详细论述了网络营销活动中的竞争情报需求,并总结了获取竞争情报的途径,供网络营销企业借鉴。     

Surveillance,documentation and privacy: an international comparative analysis of state intelligence records

The annals of modern history are replete with examples of how state agencies have constructed mechanisms to observe and document the subversive activities of internal parties or individuals as well as perceived foreign influences or threats. While the definition of “subversive” changes with governing parties, the documentation of previous systems persists in the custody of archival repositories. These files represent the lives and work of individuals, and archivists face significant moral and ethical challenges regarding their disposition. This comparative study examines cases from across the globe and throughout the twentieth century to reveal the dispositions of surveillance records. More specifically, the study identifies the main variables that impact how these files are handled. This study begins with a theoretical framework that identifies common trends in the archival literature regarding surveillance files and their place in recovery and reconciliation efforts. Definitions are provided to establish the boundaries of this work within a broad categorization of levels of access that emerged from the case studies. Each level of access is then explored more deeply using specific examples to illustrate the complexities of custody and access encountered with these records. Ultimately, the study of the disposition and access to surveillance files uncovers three interrelated themes: the power of records, the impact of archival practice and the need to fully explore the context in which those files are created and retained.