Using Technology Enabled Qualitative Research to Develop Products for the Social Good, An Overview

This paper discusses the potential benefits of the convergence of three recent trends for the design of socially beneficial products and services: the increasing application of qualitative research techniques in a wide range of disciplines, the rapid mainstreaming of social media and mobile technologies, and the emergence of software as a service. Presented is a scenario facilitating the complex data collection, analysis, storage, and reporting required for the qualitative research recommended for the task of designing relevant solutions to address needs of the underserved. A pilot study is used as a basis for describing the infrastructure and services required to realize this scenario. Implications for innovation of enhanced forms of qualitative research are presented

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

A study on Analysis and Utilization of Crowd-sourced Spatio-temporal Contexts from Social Media

兵庫県立大学大学院201

Brand User Attention Model Based on Online Text Reviews: An Empirical Study of New Energy Automobile Brands

Accurately grasping the rules of user behavior and market changes and timely adjusting decisions and strategies are the ways for brand development and innovation. In this paper, we proposed a brand user attention model based on online text review analysis. First of all, we collected and preprocessed the user comment text from the online forum. Secondly, through the LDA topic model and LDAvis visual analysis, the potential topics of user reviews were extracted, and a multi-dimensional feature analysis model was constructed to reveal the users\u27 attention features of brand products. Finally, took the new energy automobile brands as an example, the users\u27 attention features for the different new energy automobile brands were explored and the empirical study was carried out. This study found that the brand user attention model based on online text analysis can effectively extract the characteristics that brand users care about, obtain valuable business insight, and provide support for managers\u27 decision-making

Security and privacy recommendation of mobile app for Arabic speaking

There is an enormous number of mobile apps, leading users to be concerned about the security and privacy of their data. But few users are aware of what is meant by app permissions, which sometimes do not illustrate what kind of data is gathered. Therefore, users are still concerned about security risks and privacy, with little knowledge and experience of what security and privacy awareness. Users depend on ratings, which may be fake, or keep track of their sense to install an app, and an enormous number of users do not like to read reviews. To solve this issue, we propose a recommender system that reads users' reviews, and which exposes flaws, violations and third-party policies or the quality of a user's experience. In order to design and implement our recommender, we conduct a survey which supports two significant points: to detect the level of security and privacy awareness between users, and to gather new words into a dictionary of a recommender system, which assists to classify each review on the correct level, which can indeed reveal the scale of security and privacy in an app

BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

Background: Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification. Description: BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database. Conclusions: BioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the clustering of models based upon their annotations. Model deposition to the database today is advised by several publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU General Public License

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Credit Scoring with AHP and Fuzzy Comprehensive Evaluation Based on Behavioural Data from Weibo Platform

It is increasingly necessary to evaluate the customers\u27 credit. In the era of big data, Information on the Internet is commonly used to judge the credit worthiness of customers. Some users\u27 credit information is incomplete or unavailable, so credit managers cannot judge the true credit situation of these users. However, with the support of social data especially behavioural data and credit evaluation system, this problem can be effectively solved. This study used Weibo to obtain the behavioural data of Chinese users for credit evaluation. Two methods are used to calculate the credit scores of Weibo users, which are the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods. By analysing social processes and inviting experts to make decisions, we constructed a credit evaluation system to expose users\u27 behavioural characteristics. We found that the three key indexes determining the user’s social credit are personal identification, behavioural characteristics and interaction among friends. Then, AHP was used to determine the weight of each index. Finally, a static algorithm was proposed to compute the credit evaluation system of Weibo users using fuzzy comprehensive evaluation methods

The influencing factors of user loyalty on e-commerce shopping guide platform -- Case “Shenmezhidemai”

In recent years, the development of the online retail market has become more and more diversified. There are a large number of advertisements and products gathered on major e-commerce platforms. Consumers cannot efficiently select the products they want in the huge product pool, and it is also difficult for merchants to select high-quality products. The products are precisely oriented to consumers, so the e-commerce shopping guide industry has begun to spread, develop and become popular. To help consumers select high-quality goods more quickly, the e-commerce shopping guide platforms collect and integrate information and discounts for users, and provide users with decision-making suggestions. However, there is a phenomenon that users who are dissatisfied after purchasing a product because of product price reduction, quality problems or logistics problems become angry with the e-commerce shopping guide platform. As a result, users even quit and uninstall the e-commerce shopping guide platform completely. In fact, the result should be the responsibility of the merchant who sells the product. This thesis takes the e-commerce shopping guide platform “Shenmezhidemai” as the re-search object, and uses grounded theory, case study and in-depth interview to carry out this research. First of all, this thesis sorts out the relevant research on e-commerce shopping guide platform and user loyalty, and conducts an overview of the environment, development history and status quo, classification, characteristics and profit model of e-commerce shop-ping guide platform. Secondly, based on grounded theory and in-depth interview method, 20 people participated in the interview, and the interview records of about 20,000 words were obtained. Through open coding, axial coding, selective coding and other processes, the key influencing factors of e-commerce shopping guide platform user loyalty are analyzed and the theoretical model is constructed. Through the eight categories of user-related factors, information utility, system utility, platform reputation, recommending function, interactive function, price comparison function and cross-border shopping function, the model of influencing factors of e-commerce shopping guide platform user loyalty is carefully analyzed. Finally, aiming at the above eight categories, corresponding suggestions are put forward for e-commerce shopping guide platforms to cultivate and increase user loyalty. The thesis hopes to provide some implications and recommendations for the development of e-commerce shopping guide platforms