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Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story.

“Smart medical” applications refer to the fusion of technology and medicine that connects all linked sensor equipment with the patients, including those that measure physiological signals, such as blood pressure, pulse, and ECG. In addition, these physiological signal data are highly private and should be safely protected. It takes much longer to complete authentication processes in the traditional way, either based on public key infrastructure or attribute-based encryption, which is a burden for IoT devices. Hence, on the basis of attribute-based encryption, we propose lightweight authentication to shorten the time spent on authentication. Moreover, we use the patients’ data and timestamps as seeds to generate random numbers for authentication. The experiments show that the lightweight authentication using Xeon E3-1230 computer is about 4.45 times faster than complete authentication and 5.8 times faster than complete authentication when using Raspberry Pi. Our proposal significantly improves the disadvantages of IoT devices that lack computing power.

Graphical abstractThis work proposes a scheme to improve the self-organizing map algorithm. This is the overall flowchart of the proposed algorithm. The key ingredients include:1.A novel topic identification scheme.2.Lateral expansion using novel topic incompatibility measure.3.Hierarchical expansion scheme using novel cluster size and topic size criteria.Display Omitted HighlightsIncorporation of topic identification into SOM learning could be beneficial to text categorization task.Both lateral and hierarchical expansion during SOM learning were achieved according to criteria based on identified topics.The produced text hierarchies outperformed contemporary approaches in quality and performance on text categorization. Self-organizing maps (SOM) have been applied on numerous data clustering and visualization tasks and received much attention on their success. One major shortage of classical SOM learning algorithm is the necessity of predefined map topology. Furthermore, hierarchical relationships among data are also difficult to be found. Several approaches have been devised to conquer these deficiencies. In this work, we propose a novel SOM learning algorithm which incorporates several text mining techniques in expanding the map both laterally and hierarchically. On training a set of text documents, the proposed algorithm will first cluster them using classical SOM algorithm. We then identify the topics of each cluster. These topics are then used to evaluate the criteria on expanding the map. The major characteristic of the proposed approach is to combine the learning process with text mining process and makes it suitable for automatic organization of text documents. We applied the algorithm on the Reuters-21578 dataset in text clustering and categorization tasks. Our method outperforms two comparing models in hierarchy quality according to users' evaluation. It also receives better F1-scores than two other models in text categorization task.

In this paper, we address a big-data analysis method for estimating the driving range of an electric vehicle (EV), allowing drivers to overcome range anxiety. First, we present an estimating approach to project the life of battery pack for 1600 cycles (i.e., 8 years/160 000 km) based on the data collected from a cycle-life test. This approach has the merit of simplicity. In addition, it considers several critical issues that occur inside battery packs, such as the dependence of internal resistance and the state-of-health. Subsequently, we describe our work on driving pattern analysis of an EV, using a machine-learning approach, namely growing hierarchical self-organizing maps, to cluster the collected EV big data. This paper contains the analysis of energy consumption and driving range estimation for EVs, including powertrain simulation and driving behavior analysis. The experimental results, including both simulating battery degradation and analysis of driving behaviors, demonstrate a feasible solution for improving driving range estimation by the EV big data.

Social bookmarking Websites are popular nowadays for they provide platforms that are easy and clear to browse and organize Web pages. Users can add tags on Web pages to allow easy comprehension and retrieval of Web pages. However, tag spams could also be added to promote the opportunity of being referenced of a Web page, which is troublesome to users for accessing uninterested Web pages. In this work, we proposed a scheme to automatically detect such tag spams using a proposed text mining approach based on self-organizing map (SOM) model. We used SOM to find the associations among Web pages as well as tags. Such associations were then used to discover the relationships between Web pages and tags. Tag spams can then be detected according to such relationships. Experiments were conducted on a set of Web pages collected from a social bookmarking site and obtained promising result.

Due to the explosive growth of social-media applications, enhancing event-awareness by social mining has become extremely important. The contents of microblogs preserve valuable information associated with past disastrous events and stories. To learn the experiences from past events for tackling emerging real-world events, in this work we utilize the social-media messages to characterize real-world events through mining their contents and extracting essential features for relatedness analysis. On one hand, we established an online clustering approach on Twitter microblogs for detecting emerging events, and meanwhile we performed event relatedness evaluation using an unsupervised clustering approach. On the other hand, we developed a supervised learning model to create extensible measure metrics for offline evaluation of event relatedness. By means of supervised learning, our developed measure metrics are able to compute relatedness of various historical events, allowing the event impacts on specified domains to be quantitatively measured for event comparison. By combining the strengths of both methods, the experimental results showed that the combined framework in our system is sensible for discovering more unknown knowledge about event impacts and enhancing event awareness.

Highlights? We applied a density-based stream clustering method for mining Twitter data. ? The developed method can detect real-time and geospatial event features. ? Using the detection results can estimate the temporal and spatial impacts of events. ? Our method is well suited for awareness of large-scale events and risk management. Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular microblogging services such as Twitter did provide information of top trending topics for selection, it is still unable to fully support users for picking up all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to investigate how microblogging social networks (i.e. Twitter) can be used as a reliable information source of emerging events by extracting their spatio-temporal features from the messages to enhance event awareness. In this work, we applied a density-based online clustering method for mining microblogging text streams, in order to obtain temporal and geospatial features of real-world events. By analyzing the events detected by our system, the temporal and spatial impacts of the emerging events can be estimated, for achieving the goals of situational awareness and risk management.

Highlights? We developed a fusion method to integrate text and photo types of location information. ? It combines image annotation and geographic text mining. ? Photos and texts are mapped to a unified semantic space for fusion. ? The approach can associate text and image types of documents with correct geographical zones. Due to the steady increase in the number of heterogeneous types of location information on the internet, it is hard to organize a complete overview of the geospatial information for the tasks of knowledge acquisition related to specific geographic locations. The text- and photo-types of geographical dataset contain numerous location data, such as location-based tourism information, therefore defining high dimensional spaces of attributes that are highly correlated. In this work, we utilized text- and photo-types of location information with a novel approach of information fusion that exploits effective image annotation and location based text-mining approaches to enhance identification of geographic location and spatial cognition. In this paper, we describe our feature extraction methods to annotating images, and utilizing text mining approach to analyze images and texts simultaneously, in order to carry out geospatial text mining and image classification tasks. Subsequently, photo-images and textual documents are projected to a unified feature space, in order to generate a co-constructed semantic space for information fusion. Also, we employed text mining approaches to classify documents into various categories based upon their geospatial features, with the aims to discovering relationships between documents and geographical zones. The experimental results show that the proposed method can effectively enhance the tasks of location based knowledge discovery.

Traditional content-based image retrieval (CBIR) systems often fail to meet a user's need due to the 'semantic gap' between the extracted features of the systems and the user's query. The cause of the semantic gap is the failure of extracting real semantics from an image and the query. To extract semantics of images, however, is a difficult task. Most existing techniques apply some predefined semantic categories and assign the images to appropriate categories through some learning processes. Nevertheless, these techniques always need human intervention and rely on content-based features. In this paper we propose a novel approach to bridge the semantic gap which is the major deficiency of CBIR systems. We conquer the deficiency by extracting semantics of an image from the environmental texts around it. Since an image generally co-exists with accompanying texts in various formats, we may rely on such environmental texts to discover the semantics of the image. We apply a text mining process, which adopts the self-organizing map (SOM) learning algorithm as a kernel, on the environmental texts of an image to extract the semantic information from this image. Some implicit semantic information of the images can be discovered after the text mining process. We also define a semantic relevance measure to achieve the semantic-based image retrieval task. We performed experiments on a set of images which are collected from web pages and obtained promising results.

The research on automatic hypertext construction emerges rapidly in the last decade because there exists a urgent need to translate the gigantic amount of legacy documents into web pages. Unlike traditional 'flat' texts, a hypertext contains a number of navigational hyperlinks that point to some related hypertexts or locations of the same hypertext. Traditionally, these hyperlinks were constructed by the creators of the web pages with or without the help of some authoring tools. However, the gigantic amount of documents produced each day prevent from such manual construction. Thus an automatic hypertext construction method is necessary for content providers to efficiently produce adequate information that can be used by web surfers. Although most of the web pages contain a number of non-textual data such as images, sounds, and video clips, text data still contribute the major part of information about the pages. Therefore, it is not surprising that most of automatic hypertext construction methods inherit from traditional information retrieval research. In this work, we will propose a new automatic hypertext construction method based on a text mining approach. Our method applies the self-organizing map algorithm to cluster some at text documents in a training corpus and generate two maps. We then use these maps to identify the sources and destinations of some important hyperlinks within these training documents. The constructed hyperlinks are then inserted into the training documents to translate them into hypertext form. Such translated documents will form the new corpus. Incoming documents can also be translated into hypertext form and added to the corpus through the same approach. Our method had been tested on a set of at text documents collected from a newswire site. Although we only use Chinese text documents, our approach can be applied to any documents that can be transformed to a set of index terms.

Recently research on text mining has attracted lots of attention from both industrial and academic fields. Text mining concerns of discovering unknown patterns or knowledge from a large text repository. The problem is not easy to tackle due to the semi-structured or even unstructured nature of those texts under consideration. Many approaches have been devised for mining various kinds of knowledge from texts. One important aspect of text mining is on automatic text categorization, which assigns a text document to some predefined category if the document falls into the theme of the category. Traditionally the categories are arranged in hierarchical manner to achieve effective searching and indexing as well as easy comprehension for human beings. The determination of category themes and their hierarchical structures were most done by human experts. In this work, we developed an approach to automatically generate category themes and reveal the hierarchical structure among them. We also used the generated structure to categorize text documents. The document collection was trained by a self-organizing map to form two feature maps. These maps were then analyzed to obtain the category themes and their structure. Although the test corpus contains documents written in Chinese, the proposed approach can be applied to documents written in any language and such documents can be transformed into a list of separated terms.

This paper describes our work on developing a language-independent technique for discovery of implicit knowledge from multilingual information sources. Text mining has been gaining popularity in the knowledge discovery field, particularity with the increasing availability of digital documents in various languages from all around the world. However, currently most text mining tools mainly focus only on processing monolingual documents (particularly English documents): little attention has been paid to apply the techniques to handle the documents in Asian languages, and further extend the mining algorithms to support the aspects of multilingual information sources. In this work, we attempt to develop a language-neutral method to tackle the linguistics difficulties in the text mining process. Using a variation of automatic clustering techniques, which apply a neural net approach, namely the Self-Organizing Maps (SOM), we have conducted several experiments to uncover associated documents based on a Chinese corpus, Chinese-English bilingual parallel corpora, and a hybrid Chinese-English corpus. The experiments show some interesting results and a couple of potential paths for future work in the field of multilingual information discovery. Besides, this work is expected to act as a starting point for exploring the impacts on linguistics issues with the machine-learning approach to mining sensible linguistics elements from multilingual text collections.

With the continuously increasing needs of location information for users around the world, applications of geospatial information have gained a lot of attention in both research and commercial organizations. Extraction of semantics from the image content for geospatial information seeking and knowledge discovery has been thus becoming a critical process. Unfortunately, the available geographic images may be blurred, either too light or too dark. It is therefore often hard to extract geographic features directly from images. In this paper, we describe our developed methods in applying local scale-invariant features and bag-of-keypoints techniques to annotating images, in order to carry out image categorization and geographic knowledge discovery tasks. First, local scale-invariant features are extracted from geographic images as representative geographic features. Subsequently, the bag-of-keypoints methods are used to construct a visual vocabulary and generate feature vectors to support image categorization and annotation. The annotated images are classified by using geographic nouns. The experimental results show that the proposed approach is sensible and can effectively enhance the tasks of geographic knowledge discovery.

The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.

Recently knowledge discovery and data mining in unstructured or semi-structured texts(text mining) has been attracted lots of attention from both commercial and research fields. One aspect of text mining is on automatic text categorization, which assigns a text document to some predefined category according to the correlation between the document and the category. Traditionally the categories are arranged in hierarchical manner to achieve effective searching and indexing as well as easy comprehension for human. The determination of categories and their hierarchical structures were most done by human experts. In this work, we developed an approach to automatically generate categories and reveal the hierarchical structure among them. We also used the generated structure to categorize text documents. The document collection is trained by a self-organizing map to form two feature maps. We then analyzed the two maps to obtain the categories and the structure among them. Although the corpus contains documents written in Chinese, the proposed approach can be applied to documents written in any language and such documents can be transformed into a list of separated terms.