The role of personality traits in Facebook and Internet addictions: A study on Polish,Turkish, and Ukrainian samples

Although the relationships between Facebook addiction, Internet addiction and personality have already been investigated. The aim of the present study was to explore whether there were personality traits associated with Facebook addiction and Internet addiction across three different cultures. The participants were 1011 Facebook users from Poland, Turkey, and Ukraine. We administered the Bergen Facebook Addiction Scale, the Internet Addiction Test, and the Ten Item Personality Inventory. We found that in the total sample Facebook addiction was positively associated with Internet addiction, while Internet addiction was negatively associated with emotional stability, conscientiousness, and extraversion. The lack of invariance across the countries indicates that the tested links are not the same in each of them, which may suggest the role of cultural factors in this phenomenon. The results are discussed in the light of Facebook addiction and personality literature. The application of culturally sensitive intervention programs in dealing with Facebook addiction is encouraged.     

Who uses Facebook? An investigation into the relationship between the Big Five, shyness, narcissism, loneliness, and Facebook usage

The unprecedented popularity of the social networking site Facebook raises a number of important questions regarding the impact it has on sociality. However, as Facebook is a very recent social phenomenon, there is a distinct lack of psychological theory relating to its use. While research has begun to identify the types of people who use Facebook, this line of investigation has been limited to student populations. The current study aimed to investigate how personality influences usage or non-usage of Facebook. The sample consisted of 1324 self-selected Australian Internet users (1158 Facebook users and 166 Facebook nonusers), between the ages of 18 and 44. Participants were required to complete an online questionnaire package comprising the Big Five Inventory (BFI), the Narcissistic Personality Inventory - 29-item version (NPI-29), the Revised Cheek and Buss Shyness Scale (RCBS), and the Social and Emotional Loneliness Scale for Adults - Short version (SELSA-S). Facebook users also completed a Facebook usage questionnaire. The results showed that Facebook users tend to be more extraverted and narcissistic, but less conscientious and socially lonely, than nonusers. Furthermore, frequency of Facebook use and preferences for specific features were also shown to vary as a result of certain characteristics, such as neuroticism, loneliness, shyness and narcissism. It is hoped that research in this area continues, and leads to the development of theory regarding the implications and gratifications of Facebook use.     

A secure and scalable storage system for aggregate data in IoT

In recent years, with the impressive rapid development of integrated circuit and networking technologies, computers, devices and networking have become highly pervasive, incurring the introduction, development and deployment of the Internet of Things (IoT). The tiny identifying devices and wearables in IoT have transformed daily life in human society, as they generate, process and store the amount of data increasing at exponential rate all over the world. Due to high demand on data mining and analytics activities in IoT, secure and scalable mass storage systems are highly demanded for aggregate data in efficient processing. In this paper, we propose such a secure and scalable IoT storage system based on revised secret sharing scheme with support of scalability, flexibility and reliability at both data and system levels. Shamir’s secret sharing scheme is applied to achieve data security without complex key management associated with traditional cryptographic algorithms. The original secret sharing scheme is revised to utilize all coefficients in polynomials for larger data capacity at data level. Flexible data insert and delete operations are supported. Moreover, a distributed IoT storage infrastructure is deployed to provide scalability and reliability at system level. Multiple IoT storage servers are aggregated for large storage capacity whereas individual servers can join and leave freely for flexibility at system level. Experimental results have demonstrated the feasibility and benefits of the proposed system as well as tangible performance gains.     

The promising future of healthcare services: When big data analytics meets wearable technology

As policy-makers and business practitioners across the globe expend extraordinary effort toward the field of e-health, the thriving development of healthcare-wearable technology is creating great opportunities and posing a remarkable future for healthcare services. This paper employs a game theory model to investigate the dynamics of wearable device market. We extend the two-dimensional product differentiation model by incorporating consumer diversity, consumer density, and firms’ big data analytics (BDA) investment strategy. Our model reveals that with differentiated consumer densities firms are more likely to engage in quality competition and the firm that invests in BDA can achieve higher profits. Furthermore, the overall quality of biomedical and healthcare services can be improved under various market conditions. Our findings provide practical guidance to wearable device manufacturers on optimizing competition strategies and offer insights to social planners on potential policy-making to promote better healthcare services.     

一种机器人自主思维方法

类人智能一直是人工智能和机器人领域的研究重点,如何完善周边信息及对大数据的获取、处理、存储、信息挖掘更是当前研究热点。文中根据人体生物特征,利用多传感器综合对周边环境进行完整,利用数据降维与稀疏学习的技术完成大数据的处理及信息挖掘,并建立一种机器人自主思维模型。进一步提升了机器人的智能性,使之能对实时环境的空间大数据实行处理,并做出自主性反应,具有更强实用性。     

An associative engines based approach supporting collaborative analytics in the Internet of cultural things

In this paper we illustrate an integrated approach combining Business Intelligence, Big Data and Internet of Things (IoT), which is applied to information resources including structured and unstructured contents, Geo-Spatial and Social Network data, Multimedia (MM), multiple domain vocabularies, classifiers and ontologies. This is implemented in an information system which exploits Associative in-memory technologies in the context of Cloud Computing, as well as Semantic technologies for merging and analyzing information coming from heterogeneous sources. The primary aim is supporting Cultural Heritage Asset crowdsourcing, promotion, publication, management and usage. We describe and discuss, in particular, the application of this system for the analysis of behavior and interest of visitors in different types of populations and visits: on-site/ad-hoc (exhibitions, museums, cultural events) and territorial (historical downtown, archaeological or other touristic areas and routes including cultural resources). In this way it will be possible to provide a common ICT infrastructure and a set of advanced services for all types of subjects interested in the Cultural Heritage domain. The results of the experimentation encourage a Business Intelligence approach which is suitable for both nonprofit, research and business oriented organizations.     

工业建筑中大体积混凝土施工质量控制

对大体积混凝土施工中常见质量问题产生的原因进行了分析，并重点对施工质量控制要点进行了阐述。     

面向信息化战争的数据挖掘研究

信息化战争中，如何分析与利用呈级数式增长的战场数据，已经成为决定战争胜负的关键因素。本文探讨了信息化条件下，数据挖掘在获取处理军事情报、辅助分析作战仿真、监测维护网络安全等方面的应用，对信息化战场数据的处理提供了一些有益借鉴。     

A hybrid integrated architecture for energy consumption prediction

Irresponsible and negligent use of natural resources in the last five decades has made it an important priority to adopt more intelligent ways of managing existing resources, especially the ones related to energy. The main objective of this paper is to explore the opportunities of integrating internal data already stored in Data Warehouses together with external Big Data to improve energy consumption predictions. This paper presents a study in which we propose an architecture that makes use of already stored energy data and external unstructured information to improve knowledge acquisition and allow managers to make better decisions. This external knowledge is represented by a torrent of information that, in many cases, is hidden across heterogeneous and unstructured data sources, which are recuperated by an Information Extraction system. Alternatively, it is present in social networks expressed as user opinions. Furthermore, our approach applies data mining techniques to exploit the already integrated data. Our approach has been applied to a real case study and shows promising results. The experiments carried out in this work are twofold: (i) using and comparing diverse Artificial Intelligence methods, and (ii) validating our approach with data sources integration.     

The incremental validity of the dark triad in predicting driving aggression

This research tested the association between the Dark Triad and driving aggression as well as the incremental validity of the Dark Triad in predicting aggressive driving and the mediation role of the Dark Triad in the relationship between Big Five personality factors and aggressive driving. 274 undergraduate students in Study 1 and 95 amateur drivers in Study 2 completed measures of the Dark Triad (Machiavellianism, Narcissism and Psychopathy), the Big Five personality factors and the aggressive driving expression. Results showed that all the Dark Triad traits were related to aggressive driving behavior in both Study 1 and Study 2 and that the Dark Triad predicted driving aggression after the effect of the Big five personality factors was controlled, with Psychopathy being the strongest predictor of driving aggression in both Study 1 and Study 2. Machiavellianism and Psychopathy mediated the relationship between Emotional Stability, Agreeableness, Conscientiousness on one hand and aggressive driving on the other hand.