Home Appliance Control with Publish Subscribe in Social Media

 Nowadays, Internet social media has enriched the way people to communicate and interact each other. Will it be possible for people to interact with their home appliances around? This paper proposes a new approach in smart home system that made possible for people to remotely interact with their appliances using social media networks. In this paper, we present a smart home prototype system that leverages Twitter’s Application Program Interface (API) to remotely control home appliances over the Internet. Experiment results showed that the system immediately responds to remote commands sent over a social media account to control home appliances. The system responds the command in 3672.96 ms. Publish-subscribe method work better in mass announcement communication system. Home system could notice all householders in less than 6 s independenly from number of householder. Our proposed method gives alternative solution to build reliable, fast and simple control method

A method to disseminate and communicate IS research outputs beyond academia

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonAcademic researchers in many disciplines are facing difficulties in disseminating their research outputs beyond the academic community. Particularly, Information Systems (IS) academic researchers have been struggling to make their research more relevant to practice. The diversity of IS research means that should be a wide audience within and beyond academia who could benefit from IS research outputs. This audience includes educators, practitioners, patients, etc. How IS relevant to practice is a central dilemma of IS research. Research relevance is classified according to dimensions such as interesting, implementable, current, accessible “Article style” by many IS scholars. These dimensions are important to be investigated as some academic papers are yet to be beneficial to an audience beyond academia. The Accessible dimension is the focus of this study where accessible means the academic papers should be readable and understood in terms of tone, style, structure, and semantics by the potential audience beyond the academic community. This study investigates the barriers that limit academic researchers in disseminating and communicating their research outputs beyond academia. This study aims to design a communication method to assist academic researchers in disseminating and communicating their research outputs beyond academia. This study consists of three phases, in the first phase a qualitative method is applied by interviewing academics in the Information System and Computing Department at Brunel University to gain a better understanding of how and why academics disseminate beyond academia. Based on communication theories a research framework is adapted to analyse and explain the interview data. In the second phase, short videos are recorded of 10 academics where each explains one of their papers. In the third phase, two different groups are interviewed to evaluate the 10 short videos in regards the Information Quality (IQ) dimensions (i.e. appropriate amount of information, format, and timeliness). By using the thematic analysis technique the academics highlighted three barriers that limit them to disseminate and communicate their research outputs beyond academia. The three barriers are the message (i.e. academic structure and language of research papers), channel (i.e. academic journal and conferences), and social system (i.e. lack of Incentives, lack of time, and lack of support). Moreover, academics emphasised the vital role of feedback loop in their communication with target audience beyond academia. The 10 short videos are designed to overcome two of these barriers (i.e. message and channels). Each short video is evaluated by its academic author on one hand and the potential audience/stakeholders of the short video from the other hand (e.g. practitioners). Thus, the academic authors of the video suggest some changes by adjusting the video structure and adding some examples for more explanations of their research papers. Also, authors concerned about format particularly the visual elements of the video which have to be completely matched with the title of the video. However, the opinions of potential audiences vary based on their information need. For example, some practitioners are concerned with the practical information, in other words, practitioners seek the applicable part of the information provided in the short video (i.e. how to apply something); and others watch the short video to increase their awareness of a particular topic. This study will assist academic researchers to focus on how to disseminate their research outputs to audience/stakeholders beyond academia using media tools (i.e. video). Also, it provides a novel method of disseminating and communicating their research outputs beyond the academic community. Moreover, this study helps to create an interaction platform that enables academic researchers to build a collaborative framework and a mutual understanding with the audience beyond academia

A semantic conceptualization on tagged bag-of concepts to improve accuracy for sentiment Analysis

Sentiment could be expressed implicitly or explicitly in a text. The main challenge in sentiment analysis (SA) is to identify hidden sentiments. This challenge is even worsened by false classification of opinion words, neglect of context information, and poor handling of short texts. This study addresses the limitations of bag-of-words (BoW) and bag-of-concepts (BoC) text representations, in contextual and conceptual semantic methods. A semantic conceptualization method using Tagged BoC (TBoC) for SA is proposed to detect the correct sentiment towards the actual target that considers all affective and conceptual information conveyed in a text with a special focus on short text. The TBoC is an approach that analyses and decomposes text to uncover latent sentiments while preserving all relations and vital information to boost SA accuracy. In addition, the most efficient lexicons and pre-processing techniques are investigated in improving the accuracy of SA. This study comprises four phases: a) data collection and pre-processing, b) concepts extraction from text data using conceptualization method, c) documents deconstruction into TBoC using Long Short- Term Memory, Convolutional Neural Network, Latent Dirichlet Allocation, Rulebased, and customized algorithms, and d) sentiment classification on multiple benchmarking datasets. A comparative study was also conducted with state-of-the-art SA methods to evaluate the proposed approach using general-purpose and domainspecific sentiment lexicons on multiple SA levels including document, aspect, category, and topic levels. The TBoC technique with domain-specific sentiment lexicon has shown good performance and outperformed other state-of-the-art methods. Accuracy results indicated an improvement of 2%, 3%, and 6% compared to Naïve Bayes, Neural Networks, and Support Vector Machine respectively for aspect-level SA. The use of TBoC within the semantic conceptualization has high capabilities in concept extraction while preserving information on the context, interrelations, and latent feelings. Thus, contributing knowledge in SA and into the lexicon-based and hybrid approaches

Microblogging as a mechanism for human–robot interaction

This article has been made available through the Brunel Open Access Publishing Fund.This paper presents a novel approach to social data analysis, exploring the use of microblogging to manage interaction between humans and robots, and presenting and evaluating an architecture that extends the use of social networks to connect humans and devices. The approach uses natural language processing - in the form of simple grammar-based techniques - to extract features of interest from textual data retrieved from a microblogging platform in real-time and generate appropriate executable code for the robot. The simple rule-based solution exploits some of the 'natural' constraints imposed by microblogging platforms to manage the potential complexity of the interactions and create bi-directional communication. In order to evaluate the developed system, an analysis of real-time, user-generated social media data is presented. The analysis demonstrates the feasibility of producing programmes from the social media data which lead to executable actions by a front-end application - an approach of immediate relevance to web-based systems, like question-answering engines, personal digital assistants, and smart home/office devices