Jumping Finite Automata for Tweet Comprehension

Every day, over one billion social media text messages are generated worldwide, which provides abundant information that can lead to improvements in lives of people through evidence-based decision making. Twitter is rich in such data but there are a number of technical challenges in comprehending tweets including ambiguity of the language used in tweets which is exacerbated in under resourced languages. This paper presents an approach based on Jumping Finite Automata for automatic comprehension of tweets. We construct a WordNet for the language of Kenya (WoLK) based on analysis of tweet structure, formalize the space of tweet variation and abstract the space on a Finite Automata. In addition, we present a software tool called Automata-Aided Tweet Comprehension (ATC) tool that takes raw tweets as input, preprocesses, recognise the syntax and extracts semantic information to 86% success rate

Adaptive Cluster Head Selection Scheme for High Mobility Based IEEE 802.15.6 Wireless Body Area Networks

open access articleDue to the development in the field of Wireless Sensor Networks (WSNs), its major application, Wireless Body Area Network (WBAN) has presently become a major area of interest for the developers and researchers. Efficient sensor nodes data collection is the key feature of any effective wireless body area network. Prioritizing nodes and cluster head selection schemes plays an important role in WBAN. Human body exhibits postural mobility which affects distances and connections between different sensor nodes. In this context, we propose maximum consensus based cluster head selection scheme, which allows cluster head selection by using Link State. Nodal priority through transmission power is also introduced to make WBAN more effective. This scheme results in reduced mean power consumption and also reduces network delay. A comparison with IEEE 802.15.6 based CSMA/CA protocol with different locations of cluster head is presented in this paper. These results show that our proposed scheme outperforms Random Cluster head selection, Fixed Cluster head at head, Foot and Belly positions in terms of mean power consumption, network delay, network throughput and bandwidth efficiency

Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Activity recognition is essential in providing activity assistance for users in smart homes. While significant progress has been made for single-user single-activity recognition, it still remains a challenge to carry out real-time progressive composite activity recognition. This paper introduces a hybrid ontological and temporal approach to composite activity modelling and recognition by extending existing ontology-based knowledge-driven approach. The compelling feature of the approach is that it combines ontological and temporal knowledge representation formalisms to provide powerful representation capabilities for activity modelling. The paper describes in detail ontological activity modelling which establishes relationships between activities and their involved entities, and temporal activity modelling which defines relationships between constituent activities of a composite activity. As an essential part of the model, the paper also presents methods for developing temporal entailment rules to support the interpretation and inference of composite activities. In addition, this paper outlines an integrated architecture for composite activity recognition and elaborated a unified activity recognition algorithm which can support the recognition of simple and composite activities. The approach has been implemented in a feature-rich prototype system upon which testing and evaluation have been conducted. Initial experimental results have shown average recognition accuracy of 100% and 88.26% for simple and composite activities, respectively

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

open access articleCollaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems

Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis

open access articleSentiment analysis of social media textual posts can provide information and knowledge that is applicable in social settings, business intelligence, evaluation of citizens' opinions in governance, and in mood triggered devices in the Internet of Things. Feature extraction and selection is a key determinant of accuracy and computational cost of machine learning models for such analysis. Most feature extraction and selection techniques utilize bag of words, N‐grams, and frequency‐based algorithms especially Term Frequency‐Inverse Document Frequency. However, these approaches do not consider relationships between words, they ignore words' characteristics and they suffer high feature dimensionality. In this paper we propose and evaluate a feature extraction and selection approach that utilizes a fixed hybrid N‐gram window for feature extraction and minimum redundancy maximum relevance feature selection algorithm for sentence level sentiment analysis. The approach improves the existing features extraction techniques, specifically the N‐gram by generating a hybrid vector from words, Part of Speech (POS) tags, and word semantic orientation. The vector is extracted by using a static trigram window identified by a lexicon where a sentiment word appears in a sentence. A blend of the words, POS tags, and the sentiment orientations of the static trigram are used to build the feature vector. The optimal features from the vector are then selected using minimum redundancy maximum relevance (MRMR) algorithm. Experiments were carried out using the public Yelp dataset to compare the performance of the proposed model and existing feature extraction models (BOW, normal N‐grams and lexicon‐based bag of words semantic orientations). Using supervised machine learning classifiers the experimental results showed that the proposed model had the highest F‐measure (88.64%) compared to the highest (83.55%) from baseline approaches. Wilcoxon test carried out ascertained that the proposed approach performed significantly better than the baseline approaches. Comparative performance analysis with other datasets further affirmed that the proposed approach is generalizable

An Ontology-Based Hybrid Approach to Activity Modeling for Smart Homes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Activity models play a critical role for activity recognition and assistance in ambient assisted living. Existing approaches to activity modeling suffer from a number of problems, e.g., cold-start, model reusability, and incompleteness. In an effort to address these problems, we introduce an ontology-based hybrid approach to activity modeling that combines domain knowledge based model specification and data-driven model learning. Central to the approach is an iterative process that begins with “seed” activity models created by ontological engineering. The “seed” models are deployed, and subsequently evolved through incremental activity discovery and model update. While our previous work has detailed ontological activity modeling and activity recognition, this paper focuses on the systematic hybrid approach and associated methods and inference rules for learning new activities and user activity profiles. The approach has been implemented in a feature-rich assistive living system. Analysis of the experiments conducted has been undertaken in an effort to test and evaluate the activity learning algorithms and associated mechanisms

An Evaluation of Financing and Development of Small and Medium Enterprises in Mombasa County, Kenya

The purpose of this research was to establish the relationship and the link between financing institutions and the level of development, growth of small and medium enterprises in Mombasa County. In the course of the research it was found out that many factors contribute to the growth and development of SMEs in Mombasa County as it is also envisaged in other parts of the country. However the element of financing came out clearly as a major factor that contributes positively to the development of SMEs. The research covered various categories which included but not limited to formal, informal, public and private owned enterprises. It was interesting to note that most of the SMEs could not survive the third year incubation period which was attributed to lack of adequate and relevant financing information. Empirical evidence from this study suggests that SMEs operators need information on available bank loans, sources of business finance, SMEs loan schemes, information on venture capital and other types of finances. This study was realized through the use of questionnaires both open and closed ended. A descriptive research design approach was employed to collect data from service, manufacturing, commerce and trade among other small and medium enterprises in Mombasa to actualize the objectives of this research. The data was then tabulated quantitatively in form of charts, tables and percentages and analyzed using SPSS and Ms. Excel. Further to this research, it was observed that generally Mombasa County has a weak enterprise finance information system that could not support, in particular, the information needs of SMEs. The findings revealed that general knowledge and awareness of financing options available to SMEs in Mombasa County are weak. This research will be of great importance to small and medium enterprises by opening their eyes to alternative sources of finance and probably giving them a better chance of development and become competitive in the global corporate setting. It may also go a long way in helping the policy makers come up with rules and regulations governing SMEs financial development. Some recommendations that the study made include the government involvement in setting out of policies to help in finance uptake by SMEs. In conclusion, the underlying issues are that all must be involved in order to use SMEs as wealth creating instrument as portrayed in Kenya’s vision 2030 (GoK, 2007). Key Words: Financing, Development of SMEs in Mombasa, Keny

A Hybrid Ontological and Temporal Approach for Composite Activity Modelling

Activity modelling is required to support activity recognition and further to provide activity assistance for users in smart homes. Current research in knowledge-driven activity modelling has mainly focused on single activities with little attention being paid to the modelling of composite activities such as interleaved and concurrent activities. This paper presents a hybrid approach to composite activity modelling by combining ontological and temporal knowledge modelling formalisms. Ontological modelling constructors, i.e. concepts and properties for describing composite activities, have been developed and temporal modelling operators have been introduced. As such, the resulting approach is able to model both static and dynamic characteristics of activities. Several composite activity models have been created based on the proposed approach. In addition, a set of inference rules has been provided for use in composite activity recognition. A concurrent meal preparation scenario is used to illustrate both the proposed approach and associated reasoning mechanisms for composite activity recognition

A Survey on Privacy and Security of Internet of Things

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Internet of Things (IoT) has fundamentally changed the way information technology and communication environments work, with significant advantages derived from wireless sensors and nanotechnology, among others. While IoT is still a growing and expanding platform, the current research in privacy and security shows there is little integration and unification of security and privacy that may affect user adoption of the technology because of fear of personal data exposure. The surveys conducted so far focus on vulnerabilities based on information exchange technologies applicable to the Internet. None of the surveys has brought out the integrated privacy and security perspective centred on the user. The aim of this paper is to provide the reader with a comprehensive discussion on the current state of the art of IoT, with particular focus on what have been done in the areas of privacy and security threats, attack surface, vulnerabilities and countermeasures and to propose a threat taxonomy. IoT user requirements and challenges were identified and discussed to highlight the baseline security and privacy needs and concerns of the user. The paper also proposed threat taxonomy to address the security requirements in broader perspective. This survey of IoT Privacy and Security has been undertaken through a systematic literature review using online databases and other resources to search for all articles that meet certain criteria, entering information about each study into a personal database, and then drawing up tables summarizing the current state of literature. As a result, the paper distills the latest development

Dynamic Sensor Data Segmentation for Real time Activity Recognition

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The paper first analyzes the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83% in all experiments for real time activity recognition, which proves the approach and the underlying model