Extending the Similarity‐Attraction Effect: The Effects of When‐Similarity in Computer‐Mediated Communication

The feeling of connectedness experienced in computer‐mediated relationships can be explained by the similarity‐attraction effect (SAE). Though SAE is well established in psychology, the effects of some types of similarity have not yet been explored. In 2 studies, we demonstrate similarity‐attraction based on the timing of activities—“when‐similarity.” We describe a novel experimental paradigm for manifesting when‐similarity while controlling for the activities being performed (what‐similarity). Study 1 (N = 24) shows when‐similarity attraction in the evaluation of connectedness with others. Study 2 (N = 42) identifies an interaction between who‐similarity—similarity in personal backgrounds—and when‐similarity. Both studies show that real‐time computer‐mediated interaction can lead to greater feelings of connectedness between people when there is an opportunity to discover when‐similarity.     

Probabilistic performance prediction model for Austrian torrent control infrastructure

Due to the growing stock of torrent control structures and decreasing investments, the protection level can be reduced in the long term. Therefore, the focus of the future investments has been shifted on efficiency improvement and maintenance of existing structures. The maintenance of the existing structures is necessary to ensure safety from natural hazards, which are increasing as a consequence of the climate change. Due to the increasing number of structures, there is a strong need to develop a good maintenance management for these infrastructure works. This study is based on data from the condition rating programme of the Austrian Service for Torrent and Avalanche Control. For the structures relevant in this work, 75,343 records were available. The data were examined for different construction type and construction materials. The reliability of the structures was determined by means of the Markov chain. The study describes a homogeneous Markov chain model formed by the data of the condition rating. The result of this study is a proposal for a time to failure (TTF) and a time to repair (TTR) for several construction types with different construction materials as well as the time-dependent use of construction materials in the sector of the Austrian torrent control infrastructure.     

Smart cyber society: Integration of capillary devices with high usability based on Cyber–Physical System

The recent development in the field of embedded devices, such as sensors, actuators, and smartphones, etc. is providing a great business potential towards the new era of web of things (WoT); in which all the capillary devices (electronic devices) are capable of interconnecting and communicating with each other over the Internet. Therefore, web technologies provide a way towards integrating and sharing a common communication medium. However, for integrating Cyber–Physical System (CPS) and WoT, a comprehensive architecture and platform is still missing. Therefore, this paper proposes the concept of Smart Cyber Society; propelling the concept of smart home. We then propose the virtual communication platform that is composed of six functional communication layers, which provides a common medium for communication, i.e., same communication language. In addition, a system architecture for smart cyber society is also proposed, which consists of three networked domains, such as cyber home domain (networked-home), cyber society domain (networked of various societies, i.e., hospitals, police station, and fire brigade), and cyber mobile domain (networked of vehicles). Furthermore, the feasibility and efficiency of the proposed system are implemented on Hadoop single node setup on UBUNTU 14.04 LTS core^TMi5 machine with 3.2 GHz processor and 4 GB memory. Sample medical, sensory datasets and fire detection datasets are tested on the proposed system. Finally, the results show that the proposed system architecture efficiently processes, analyzes, and integrates different datasets efficiently and triggers actions to provide safety measurements for elderly age people, vehicles and others.     

DL-Learner—A framework for inductive learning on the Semantic Web

In this system paper, we describe the DL-Learner framework, which supports supervised machine learning using OWL and RDF for background knowledge representation. It can be beneficial in various data and schema analysis tasks with applications in different standard machine learning scenarios, e.g. in the life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework. The article gives an overview of the framework with a focus on algorithms and use cases.     

Big label: categorizing the Web efficiently and accurately

Searching on the Web has never been an easy task. Even if semantic information is successfully inferred from a user query, how can we benefit from it? The most popular remedy today is to categorize the Web in advance. By gathering similar Web resources into a group, the search performance should increase even though search engines still have little idea about the semantics part. To categorize a set of Web resources according to meta-information associated with them, at first, one has to analyze the relationships between meta-information and Web resources. However, the result will be severely affected by the ambiguous nature of the Web. As a result, the goal of this research is to propose a new labeling method to enhance both the efficiency and accuracy of Web resources categorization.     

Towards a trust evaluation middleware for cloud service selection

With the advent of cloud computing, employing various cloud services to build highly reliable cloud applications has become increasingly popular. The trustworthiness of cloud services is a critical issue that hinders the development of cloud applications, and thus is an urgently-required research problem. Previous studies evaluate trustworthiness of services via either QoS monitoring mechanisms or user feedback ratings, while seldom they combine both of them for enhancing service trust evaluation. This paper proposes a trustworthy selection framework for cloud service selection, named TRUSS. Aiming at developing an effective trust evaluation middleware for TRUSS, we propose an integrated trust evaluation method via combining objective trust assessment and subjective trust assessment. The objective trust assessment is based on QoS monitoring, while the subjective trust assessment is based on user feedback ratings. Experiments conducted using a synthesized dataset show that our proposed method significantly outperforms the other trust and reputation methods.     

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization’s environment and that they appear in different contexts. We use internet information to represent organization’s environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time.     

基于Web标准的CSS样式优化分析

CSS学起来并不难,但在实际开发的项目中,不同的人在CSS书写风格上会有不同,导致CSS样式臃肿而杂乱,使团队成员的工作效率下降。为此本文总结了一些如何对CSS代码进行优化的方法,以实现CSS代码易读且规范,从而提高项目开发效率。