Search

PurposeMeasuring the similarity between two resources is considered difficult due to a lack of reliable information and a wide variety of available information regarding the resources. Many approaches have been devised to tackle such difficulty. Although content-based approaches, which adopted resource-related data in comparing resources, played a major role in similarity measurement methodology, the lack of semantic insight on the data may leave these approaches imperfect. The purpose of this paper is to incorporate data semantics into the measuring process.Design/methodology/approachThe emerged linked open data (LOD) provide a practical solution to tackle such difficulty. Common methodologies consuming LOD mainly focused on using link attributes that provide some sort of semantic relations between data. In this work, methods for measuring semantic distances between resources using information gathered from LOD were proposed. Such distances were then applied to music recommendation, focusing on the effect of various weight and level settings.FindingsThis work conducted experiments using the MusicBrainz dataset and evaluated the proposed schemes for the plausibility of LOD on music recommendation. The experimental result shows that the proposed methods electively improved classic approaches for both linked data semantic distance (LDSD) and PathSim methods by 47 and 9.7%, respectively.Originality/valueThe main contribution of this work is to develop novel schemes for incorporating knowledge from LOD. Two types of knowledge, namely attribute and path, were derived and incorporated into similarity measurements. Such knowledge may reflect the relationships between resources in a semantic manner since the links in LOD carry much semantic information regarding connecting resources.

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.

As electric vehicle (EV) emerges, it is important to understand how driver's driving behavior is influencing power consumption in an electric vehicle. Driver's personal driving behavior is usually quite distinctive and can be recognized by means of driving patterns after some driving cycles. This paper presents a method combining several machine learning approaches to characterize driving behaviors of electric vehicles. The driving patterns are modeled according to power consumption monitored by the battery management system (BMS), in aspects of individual driver's personal and EV-fleet operations. First, we apply an unsupervised clustering approach to characterize a driver's behaviors by formulating driving patterns. Subsequently, the resulting clustered datasets were used to train machine-learning based classifiers for classification of dataset of EV and EV-fleet driving patterns. The work aims to provide a robust solution to help identify the characteristics of specific types of EVs and their driver behaviors, in order to allow automakers and EV-subsystem providers to gather valuable driving information for product improvement.

Recently, approaches of applying personality on practical applications have attracted attention from multidisciplinary areas beyond psychology. An example is the personality-based recommender systems that have been successfully applied to music, movie, and game recommendation tasks. However, finding the personality traits of a person is not a trivial task. In this work, we will propose an automatic scheme for identifying the Big Five personality traits using text mining techniques. We analyzed the social messages posted by Facebook users to find the correlations among keywords and personality traits. The personality traits of a person can then be identified by examining his social messages according to such correlations. We performed the experiments using the myPersonality dataset and obtained promising results.

Introduction Predicting the sentiments and emotions of people from their texts is a critical issue in cognitive computing. The explosive growth of social network services has led to a tremendous increase of textual data, increasing the demand of the advanced analysis of these data. Sentiment analysis on textual social media data emerged in recent years to fulfill the needs of areas such as national security, business, politics, and economics; however, text messages from social networks are rather different from those of traditional text documents, especially in presentation style and lengths. Therefore, it is difficult but essential to develop an effective method to explore the sentiments of social messages. Methods In this study, we first applied a self-organizing map (SOM) algorithm to cluster social messages as well as sentiment keywords. An association discovery process was then applied to discover the associations between a message and some sentiment keywords, and the sentiment of a message was determined according to such associations. Results We performed experiments on collected Twitter messages and the results’ accuracy outperformed that of a similar approach. Conclusions A sentiment analysis approach based on SOMs was proposed. The associations between messages and keywords were derived using the proposed method. The novelty of this work arises from the adoption of association discovery process in sentiment analysis.

This study aims to extract symptom profiles and functional impairments of major depressive disorder from electronic health records (EHRs). A chart review was conducted by three annotators on 500 discharge notes randomly selected from a medical center in Taiwan to compile annotated corpora for nine depressive symptoms and four types of functional impairment. Named entity recognition techniques including the dictionary-based approach., a conditional random field model, and deep learning approaches were developed for the task of recognizing depressive symptoms and functional impairments from EHRs. The results show that the average micro-F-measures of the supervised learning approaches in extracting depressive symptoms is almost perfect (>0.90) but less accurate for the extraction of functional impairment.

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.

In an era of growing concern over climate change, several utility companies originally supplied wholesale and retail power mainly made by burning coal, have started to consider and build the clean-energy power systems for resolving global warming problems. Wind power is nowadays regarded as one of the predominant alternative sources of clean energy. In this paper, we discuss our work on utilizing climate big-data associated with wind power, collected from several wind farms over four years, for exploring the correlation between regional wind speed and wind power. Once this huge amount of data are analyzed, it can be used to develop policies for siting wind-power facilities, designing smart charging algorithms, or evaluating the capacity of electrical distribution systems to meet the actual requirement of power load. Our work started with collecting related climate data for building data model to perform analytics work and experiments using Support Vector Regression (SVR) method. Also, we observed the correlations between other factors related to wind speed and wind energy from our empirical model. The preliminary experimental results demonstrate that our developed system framework is workable, allowing for detailed analysis of the important wind-power related factors on specific wind farm regions.

This paper presents a machine learning approach to analyze driving behaviors that allows a better understanding of electric vehicle. We implement a pattern recognition process to model the driving pattern according to the energy consumption for both a single driver and a fleet. The growing hierarchical self-organizing maps (GHSOM) is applied to learn driver's behaviors gradually, and the experimental results show that the driving behaviors could be recognized with the increase of driving cycles. Moreover, the proposed framework would enhance the understanding of driver's behaviors and also facilitate the EV system design, the big data analytics for IoV and the implementation of advanced driver assistant system (ADAS).

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.