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The purpose of this paper is to effectively deal with querying of classification with membership.

The authors propose a scheme comprising a layer of Bloom filter for membership checking and a second layer based on neural network for dealing with the classification requirement.

Not only could false positives be dramatically decreased, but also classification could be achieved with the proposed scheme.

The experimental data were randomly generated instead of real‐world ones.

It is difficult to implement this scheme in a real‐world environment, such as the internet. Second, the neural network requires time to converge to a satisfactory level.

Internet ethic might be compromised by hackers once they find a way around the filtering mechanism.

The neural network was moditified and utilized for the first time to be suitable for our purpose. Second, the two‐layer design shows effectiveness.

The purpose of this paper is to present a vision for the future development of Kybernetes under a new editorship.

The new Editors are introduced, the strengths and history of the journal reviewed, and plans for its future development described.

The future of Kybernetes will build on its long and distinguished heritage, noting especially the strengths of interdisplinarity, internationality, and strong links with major cybernetic societies across the world. While maintaining these strengths, the new Editors will seek to develop further the conversations between diverse fields contributing to the journal and to bring a new emphasis to the interdisciplinary study of information, to studies of the social implications of cybernetics and related fields, and to profiles of thinkers in cybernetics, systems and management science.

This is only the second time that there has been a change of editor in the more than 40 years that Kybernetes has been published. The journal (and the whole field of cybernetics and systems) owes the past editors a great debt of thanks for their outstanding work, but the time has come for change. This paper starts to identify new directions under the new Editors.

A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

For high crack-susceptibility materials such as Inconel 713LC (IN713LC) nickel alloy, fabricating crack-free components using the laser powder bed fusion (LPBF) technique represents a significant challenge because of the complex interactions between the effects of the main processing parameters, namely, the laser power and scanning speed. Accordingly, this study aims to build up a methodology which combines simulation model and experimental approach to fabricate high-density (>99.9%) IN713LC components using LPBF process.

The present study commences by performing three-dimensional (3D) heat transfer finite element simulations to predict the LPBF outcome (e.g. melt pool depth, temperature and mushy zone extent) for 33 representative sample points chosen within the laser power and scanning speed design space. The simulation results are used to train a surrogate model to predict the LPBF result for any combination of the processing conditions within the design space. Then, experimental trials were performed to choose the proper hatching space and also to define the high crack susceptibility criterion. The process map is then filtered in accordance with five quality criteria, namely, avoiding the keyhole phenomenon, improving the adhesion between the melt pool and the substrate, ensuring single-scan-track stability, avoiding excessive melt pool evaporation and suppressing the formation of micro-cracks, to determine the region of the process map which improves the relative density of the IN713LC component and minimizes the micro-cracks. The optimal processing conditions are used to fabricate IN713LC specimens for tensile testing purposes.

The optimal processing conditions predicted by simulation model are used to fabricate IN713LC specimens for tensile testing purposes. Experimental results show that the tensile strength and elongation of 3D-printed IN713LC tensile bar is higher than those of tensile bar made by casting. The yield strength of 791 MPa, ultimate strength of 995 MPa, elongation of 12%, and relative density of 99.94% are achieved.

The present study proposed a systematic methodology to find the processing conditions that are able to minimize the formation of micro-crack and improve the density of the high crack susceptivity metal material in LPBF process.

This paper aims to improve a sentiment analysis (SA) system to help users (i.e. customers or hotel managers) understand hotel evaluations. There are three main purposes in this paper: designing an unsupervised method for extracting online Chinese features and opinion pairs, distinguishing different intensities of polarity in opinion words and examining the changes in polarity in the time series.

In this paper, a review analysis system is proposed to automatically capture feature opinions experienced by other tourists presented in the review documents. In the system, a feature-level SA is designed to determine the polarity of these features. Moreover, an unsupervised method using a part-of-speech pattern clarification query and multi-lexicons SA to summarize all Chinese reviews is adopted.

The authors expect this method to help travellers search for what they want and make decisions more efficiently. The experimental results show the F-measure of the proposed method to be 0.628. It thus outperforms the methods used in previous studies.

The study is useful for travellers who want to quickly retrieve and summarize helpful information from the pool of messy hotel reviews. Meanwhile, the system will assist hotel managers to comprehensively understand service qualities with which guests are satisfied or dissatisfied.

In academic work, it is important to identify a specific domain of research. Many researchers may look to conference issues to determine interesting or new topics. Furthermore, conference issues can help researchers identify current research trends in their field and learn about cutting-edge developments in their area of specialization. However, so much conference information is published online that it can be difficult to navigate and analyze in a meaningful or productive way. Hence, the use of knowledge management (KM) could be a way to resolve these issues. In KM, ontology is widely adopted, but most ontology construction methods do not consider social information between target users. Therefore, this study aims to propose a novel method of constructing research topic maps using an open directory project (ODP) and social information.

The approach is to incorporate conference information (i.e. title, keywords and abstract) as sources and to consider the ways in which social information automatically produces research topic maps. The methodology can be divided into four modules: data collection, element extraction, social information analysis and visualization. The data collection module collects the required conference data from the internet and performs pre-processing. Then, the element extraction module extracts topics, associations and other basic elements of topic maps while considering social information. Finally, the results will be shown in the visualization module for researchers to browse and search.

The results of this study propose three main findings. First, creating topic maps with the ODP category information can help capture a richer set of classification associations. Second, social information should be considered when constructing topic maps. This study includes the relationship among different authors and topics to support information in social networks. By considering social information, such as co-authorship/collaborator, this method helps researchers find research topics that are unfamiliar but interesting or potential cooperative opportunities in the future. Third, this study presents topic maps that show a clear and simple pathway in interested domain knowledge.

First, this study analyzes and collects conference information, including the titles, keywords and abstracts of conference papers, so the data set must include all of the abovementioned information. Second, social information only analyzes co-authorship associations (collabship associations); other social information could be extracted in the future study. Third, this study only analyzes the associations between topics. The intensity of associations is not discussed in the study.

The study will have a great impact on learned societies because it bridges the gap between theory and practice. The study is useful for researchers who want to know which conferences are related to their research. Moreover, social networks can help researchers expand and diversify their research.

In community question and answer (CQA) services, because of user subjectivity and the limits of knowledge, the distribution of answer quality can vary drastically – from highly related to irrelevant or even spam answers. Previous studies of CQA portals have faced two important issues: answer quality analysis and spam answer filtering. Therefore, the purposes of this study are to filter spam answers in advance using two-phase identification methods and then automatically classify the different types of question and answer (QA) pairs by deep learning. Finally, this study proposes a comprehensive study of answer quality prediction for different types of QA pairs.

This study proposes an integrated model with a two-phase identification method that filters spam answers in advance and uses a deep learning method [recurrent convolutional neural network (R-CNN)] to automatically classify various types of questions. Logistic regression (LR) is further applied to examine which answer quality features significantly indicate high-quality answers to different types of questions.

There are four prominent findings. (1) This study confirms that conducting spam filtering before an answer quality analysis can reduce the proportion of high-quality answers that are misjudged as spam answers. (2) The experimental results show that answer quality is better when question types are included. (3) The analysis results for different classifiers show that the R-CNN achieves the best macro-F1 scores (74.8%) in the question type classification module. (4) Finally, the experimental results by LR show that author ranking, answer length and common words could significantly impact answer quality for different types of questions.

The proposed system is simultaneously able to detect spam answers and provide users with quick and efficient retrieval mechanisms for high-quality answers to different types of questions in CQA. Moreover, this study further validates that crucial features exist among the different types of questions that can impact answer quality. Overall, an identification system automatically summarises high-quality answers for each different type of questions from the pool of messy answers in CQA, which can be very useful in helping users make decisions.