Department of Computing DoC

Cloud technology is successful for data processing intensive applications having requirement of high scalability, where conventional software systems cannot perform better. This paper presents software engineering challenges faced by cloud applications, and proposes a cloud based architecture – to overcome these challenges – for a sample cloud application of earthquake forecasting.  In past few years cloud computing has emerged as distributed environment, with great potential for providing IT services over the internet. Generally, cloud consists of thousands of computing machines to provide virtually unlimited computing resources.  The advantages of using cloud includes: reduced infrastructure cost, reduced maintenance cost, reduced risk, and high scalability. To give better understanding, this paper also describes evolution of cloud computing, advantages of cloud technology, and comparison of commercial cloud providers

The significance of social media has already been proven in provoking transformation of public opinion for developed countries in improving democratic process of elections. On the contrary, developing countries lacking basic necessities of life possess monopolistic electoral system in which candidates are elected based on tribes, family backgrounds, or landlord influences. They extort voters to cast votes against their promises for the provision of basic needs. Similarly voters also poll votes for personal interests being unaware of party manifesto or national interest. These issues can be addressed by social media, resulting as ongoing process of improvement for presently adopted electoral procedures. People of Pakistan utilized social media to garner support and campaign for political parties in General Elections 2013. Political leaders, parties, and people of Pakistan disseminated party's agenda and advocacy of party's ideology on Twitter without much campaigning cost. To study effectiveness of social media inferred from individual's political behavior, large scale analysis, sentiment detection & tweet classification was done in order to classify, predict and forecast election results. The experimental results depicts that social media content can be used as an effective indicator for capturing political behaviors of different parties Positive, negative and neutral behavior of the party followers as well as party's campaign impact can be predicted from the analysis. The analytical findings proved to be having considerable correspondence with actual results as published by Election Commission of Pakistan..

The emergence of smartness in many domains for facilitating the life has boomed in recent years, one of prominent area is Healthcare. Many of the developed applications for healthcare, use diagnosis/recommendation mechanism for facilitating physicians and patients. We propose a novel approach that reinforces physician diagnosis, guidelines and recommendation by Semantic knowledge inferencing. Upon receiving any diagnosis/recommendation our model unveils hidden knowledge through reasoning over Domain Specific Clinical Model DSCM thus enhancing authenticity of clinical decisions. This approach provides first-time ever combination of rule-based reasoning, automatic reasoner selection and updation of DSCMs. The clinical domain consists of heterogeneous DSCMs also termed as Ontologies.

The significance of social media has already been proven in provoking transformation of public opinion for developed countries in improving democratic process of elections. On the contrary, developing countries lacking basic necessities of life possess monopolistic electoral system in which candidates are elected based on tribes, family backgrounds, or landlord influences. They extort voters to cast votes against their promises for the provision of basic needs. Similarly voters also poll votes for personal interests being unaware of party manifesto or national interest. These issues can be addressed by social media, resulting as ongoing process of improvement for presently adopted electoral procedures. People of Pakistan utilized social media to garner support and campaign for political parties in General Elections 2013. Political leaders, parties, and people of Pakistan disseminated party's agenda and advocacy of party's ideology on Twitter without much campaigning cost. To study effectiveness of social media inferred from individual's political behavior, large scale analysis, sentiment detection & tweet classification was done in order to classify, predict and forecast election results. The experimental results depicts that social media content can be used as an effective indicator for capturing political behaviors of different parties Positive, negative and neutral behavior of the party followers as well as party's campaign impact can be predicted from the analysis. The analytical findings proved to be having considerable correspondence with actual results as published by Election Commission of Pakistan..

There is sufficient evidence proving the impact that negative lifestyle choices have on people's health and wellness. Changing unhealthy behaviours requires raising people's self-awareness and also providing healthcare experts with a thorough and continuous description of the user's conduct. Several monitoring techniques have been proposed in the past to track users' behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user's context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels. The last World Health Organization (WHO) global status report on noncommunicable diseases reveals that illnesses associated with lifestyle choices are currently the leading cause of death worldwide [1]. As a matter of fact, non-communicable diseases are responsible for more than two-thirds of the world's deaths, with more than 40% of them representing premature deaths under the age of 70 years. Recognizing this seriously worrying epidemic scenario, the WHO has defined a clear roadmap to alter the course of the so-called " slow-moving public health disaster ". Most of the policies

Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is required to scale to long-term scenarios with a large number of users.

Case-Based Learning (CBL) has become an effective pedagogy for student-centered learning
in medical education, which builds its foundation on persisted patient cases. Flip learning and
Internet of Things (IoTs) concepts have gained much attention in the recent years. These concepts with CBL can improve learning capabilities by providing real and evolutionary medical
cases. It also enables students to build confidence in decision making, and to enhance teamwork environment efficiently. This paper proposes an IoT-based Flip Learning Platform, called
IoTFLiP, where IoT infrastructure is exploited to support Flipped case-based learning in cloud
environment with state of the art security and privacy measures for the potential personalized
medical data. It also provides the support for application delivery in private, public, and hybrid
approaches. The proposed platform is the extension of our Interactive Case-Based Flip Learning Tool (ICBFLT) that is developed based on the current CBL practices. ICBFLT formulates the
summaries of CBL cases through synergies of students’ as well as medical experts’ knowledge.
Due to low cost and with reduced sensing devices’ size, support of IoTs, and recent flip learning concepts can enhance medical students’ academic and practical experiences. To demonstrate
the working scenario of proposed IoTFLiP platform, a real-time data through IoTs gadgets is
collected to generate a real-life situation case for a medical student using ICBFLT.

While reasoners are year after year scaling up in the classical, time invariant domain of ontological knowledge, reasoning upon rapidly dynamic information has been neglected, forgotten or less attention given. On the contrary, processing of data streams has been largely investigated and specialized Database Stream Management Systems (DSMS) and Complex Event Processing (CEP) also exist. In this paper, by coupling reasoners with powerful, reactive, throughput-efficient DSMS, we propose conceptual model for Real time stream reasoning over RDF streams obtained from heterogeneous data streams. This conceptual model is believed to be having capability of handling continuous query answering in real-time with an efficient way and having better throughput.

The emerging research on automatic identification of user's contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user's contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.