The connection of the style of interactions and the collaboration in a virtual work environment

Kiss, L.; Hamornik, Balazs Peter; Geszten, Dalma; Hercegfi, Károly

doi:10.1109/coginfocom.2015.7390592

Cited by 2 publications

(1 citation statement)

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“…In the paper, ethical considerations related to the privacy of participants and the anonymity of individuals are mentioned. [16] mentions that to realize the social interactions in a virtual workspace, anonymity can influence the process' outputs significantly.…”

Section: Resultsmentioning

confidence: 99%

Recognition Technique of ConfidentialWords Using Neural Networksin Cognitive Infocommunications

2019

APH

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Cognitive Infocommunications (CogInfoCom) involves communication, especially the combination of informatics and telecommunications. In the future, infocommunication is expected to become more intelligent and supportive of life. Privacy is one of the most critical concernsin infocommunications. A well-recognized technology that ensures privacy is encryption; however, it is not easy to hide personal information completely. One technique to protect privacy is to find confidential words in a file or a website and change them into meaningless words. In this paper, we use a judicial precedent dataset from Japan to discuss a recognition technique for confidential words using neural networks. The disclosure of judicial precedents is essential, but only some selected precedents are available for public viewing in Japan. One reason for this is the concern for privacy. Japanese values do not allow the disclosure of the individual's name and address present in the judicial precedents dataset. However, confidential words, such as personal names, corporate names, and place names, in the judicial precedents dataset are converted into other words. This conversion is done manually because the meanings and contexts of sentences need to be considered, which cannot be done automatically. Also, it is not easy to construct a comprehensive dictionary for detecting confidential words. Therefore, we need to realize an automatic technology that would not depend on a dictionary of proper nouns to ensure that the confidentiality requirements of the judicial precedents are not compromised. In this paper, we propose two models that predict confidential words by using neural networks. We use long short-term memory (LSTM) and continuous bag-ofwords (CBOW) as our language models. Firstly, we explain the possibility of detecting the words surrounding an confidential word by using CBOW.

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Section: Resultsmentioning

confidence: 99%