For the upcoming conference on Intelligence Studies at ICI 2020 in Bad Nauheim, Germany the focus of this issue of JISIB is on collective intelligence and foresight. The first two papers by Soilen and Almedia and Lesca deal with collective intelligence from an intelligence studies perspective. It may be said that the Internet itself is a gigantic collective intelligence effort, the largest in human history. Open source is a prerequisite for this system to work for everyone. The article by Cerný et al. is on open source. All other contributions are on the connection between the Internet, software and intelligence. This issue consists of seven articles to compensate for two articles that were taken out by editors in the last issue. The first article by Soilen entitled “Making sense of the collective intelligence field: a review” is a historical review of the field of collective intelligence. The paper shows how collective intelligence is an interdisciplinary field and argues there is a flaw in the notion of “wisdom of crowds”. Collective intelligence can be understood in terms of social systems theory and as such this approach has been fruitful for the social sciences, although so far not very popular. It also bares relevance for the study of business and economics. The second article by Almeida and Lesca is entitled “Collective intelligence process to interpret weak signals and early warnings”. Early warning and the detection of weak signals is a vital topic for any intelligence organization. Two aspects are discussed in the paper, the importance of new technology and collective sense making or interpretation The third article by Shaikh and Singhal entitled “Study on the various intellectual property management strategies used and implemented by ICT firms for business intelligence” deals with intellectual property rights and patenting strategies. The authors identify a number of defensive and offensive IP strategies applied to ICT companies. The results have a bearing on patent acquisitions. The fourth article by Lamrhari et al. is entitled “Web intelligence for understanding customer satisfaction: application of Latent Dirichlet Allocation (LDA) and the Kano model”. Customer satisfaction today is mostly measured with data from the internet, using different business intelligence techniques. The Kano model is still valuablei,ii, but the way we gather information to assess the different levels in the model has changed. The authors use Latent Dirichlet Allocation to analyze the voice of customer (VOC) in online reviews. They suggest that BI techniques and a fuzzy-Kano model can enable companies to better understand their customers’ online reviews. The fifth article by Nahili et al. is entitled “A new corpus-based convolutional neutral network for big data text analysis”. Companies need efficient ways to analyze everything that is said about them on the internet (reviews, comments). The paper suggests a convolutional neural network (CNN) as it has been successfully used for text classification. IMDB movie reviews and Reuters datasets were used for the experiment. The sixth article by Cerný et al. is entitled “Using open data and google search data for competitive intelligence analysis”. Taking the Czech antidepressant market as an example, the authors show how competitive intelligence can be obtained using Google Search data, Google Trend and other OSINT sources. The seventh article by Dadkhah et al. is entitled “The potential of business intelligence tools for expert findings”. The paper suggests a way for researchers to find experts using business intelligence tools. The same method may also be used by any business or person looking for experts on a specific topic. As always, we would above all like to thank the authors for their contributions to this issue of JISIB. Thanks to Dr. Allison Perrigo for reviewing English grammar and helping with layout design for all articles and to the Swedish Research Council for continuous financial support. We hope to see you all at the ICI 2020 on the 16-17 March, 2020. The deadline for the two-page abstract submission is March 1st, 2020.

中文翻译：

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更深入地了解集体智慧现象

对于即将在德国巴特瑙海姆举行的ICI 2020情报研究会议，本期JISIB的重点是集体情报和远见。Soilen和Almedia以及Lesca的前两篇论文从情报研究的角度探讨了集体情报。可以说，互联网本身是一项巨大的集体情报工作，是人类历史上最大的努力。开源是该系统为每个人工作的前提。Cerný等人的文章。是开源的。所有其他贡献都与Internet，软件和情报之间的连接有关。本期由七篇文章组成，以弥补上一期编辑删除的两篇文章。Soilen的第一篇文章名为“了解集体智慧领域：评论”是对集体智慧领域的历史回顾。该论文展示了集体智慧如何成为一个跨学科领域，并辩称“人群的智慧”概念存在缺陷。可以从社会系统理论的角度来理解集体智慧，因此，尽管到目前为止，这种方法对于社会科学还是富有成果的。它也与商业和经济学研究无关。Almeida和Lesca撰写的第二篇文章的标题为“解释弱信号和预警的集体情报过程”。预警和微弱信号的检测是任何情报组织的重要课题。本文讨论了两个方面：新技术的重要性以及集体意识的形成或解释的重要性Shaikh和Singhal撰写的第三篇文章“研究ICT公司用于商业智能的各种知识产权管理策略的研究”涉及知识产权和专利策略。作者确定了适用于ICT公司的许多防御性和进攻性IP策略。结果与专利获取有关。Lamrhari等人的第四篇文章。标题为“用于理解客户满意度的网络智能：潜在狄利克雷分配（LDA）和Kano模型的应用”。如今，客户满意度主要是通过使用不同的商业智能技术，来自互联网的数据来衡量的。卡诺模型仍然很有价值，ii，但是我们收集信息以评估模型中不同级别的方式已经改变。作者使用潜在Dirichlet分配来分析在线评论中的客户声音（VOC）。他们认为，BI技术和模糊Kano模型可以使公司更好地了解其客户的在线评论。Nahili等人的第五篇文章。题为“一种用于大数据文本分析的基于语料库的新卷积神经网络”。公司需要有效的方法来分析互联网上关于它们的所有评论（评论，评论）。本文提出了卷积神经网络（CNN），因为它已成功用于文本分类。IMDB电影评论和路透数据集用于实验。Cerný等人的第六篇文章。标题为“使用开放数据和Google搜索数据进行竞争情报分析”。以捷克的抗抑郁药市场为例，作者展示了如何使用Google搜索数据，Google Trend和其他OSINT来源获得竞争情报。Dadkhah等人的第七篇文章。标题为“商业智能工具对专家发现的潜力”。该论文为研究人员提供了一种使用商业智能工具寻找专家的方法。寻找特定主题的专家的任何企业或个人也可以使用相同的方法。与往常一样，我们首先要感谢作者为这一期JISIB所做的贡献。多亏了博士 Allison Perrigo审核了英语语法，并为所有文章提供了版式设计帮助，并向瑞典研究委员会提供了持续的财务支持。我们希望在2020年3月16日至17日的ICI 2020上与大家见面。两页摘要提交的截止日期为2020年3月1日。