Smartphones are context-aware devices that provide a compelling platform for ubiquitous computing and assist users in accomplishing many of their routine tasks anytime and anywhere, such as sending and receiving emails. The nature of tasks conducted with these devices has evolved with the exponential increase in the sensing and computing capabilities of a smartphone. Due to the ease of use and convenience, many users tend to store their private data, such as personal identifiers and bank account details, on their smartphone. However, this sensitive data can be vulnerable if the device gets stolen or lost. A traditional approach for protecting this type of data on mobile devices is to authenticate users with mechanisms such as PINs, passwords, and fingerprint recognition. However, these techniques are vulnerable to user compliance and a plethora of attacks, such as smudge attacks. The work in this paper addresses these challenges by proposing a novel authentication framework, which is based on recognizing the behavioral traits of smartphone users using the embedded sensors of smartphone, such as Accelerometer, Gyroscope and Magnetometer. The proposed framework also provides a platform for carrying out multi-class smart user authentication, which provides different levels of access to a wide range of smartphone users. This work has been validated with a series of experiments, which demonstrate the effectiveness of the proposed framework.

Despite significant efforts to initiate electronic government projects, developing countries are still struggling to reap the benefits of using e-government services. An effective implementation of e-government infrastructure is necessary to increase the efficiency and transparency of the government services. There are several studies that observed causes like lack of infrastructure support, lack of payment gateway and improper e-government service delivery channel as main barriers to a wider adoption of e-government services. The main contribution of this research is to propose a cloud-based G2G (Government-to-government) e-government framework for a viable e-government solution from the perspective of developing countries. We have introduced a list of concepts and a systematic process to guide the implementation of e-government project based on the government’s vision, goals, chosen services through the service delivery channel to the appropriate cloud service and deployment model. We have used Nepal as a context of the case study and applied the framework to a real e-government project of driving licensing department using action research methodology. The results from the study show that the G2G approach of e-government implementation would be the best for providing effective government services to the stakeholders of developing countries. The proposed framework also supports a smooth integration of government services and reduces the time of the overall project.

Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This paper presents a hybrid framework for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. The evaluation of the proposed framework on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques.

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain process, and describes causal dependencies between the various activities as performed within the process execution environment. In principle, one can use process discovery to obtain process models that describes reality. To this end, the work in this artcle presents a Fuzzy-BPMN mining approach that uses training events log representing 10 different real-time business process executions to provide a method for discovery of useful process models, and then cross-validating the derived models with a set of test event logs in order to measure the accuracy and performance of the employed approach. The method focuses on carrying out a classification task to determine the traces, i.e. individual cases that makes up the test event logs in order to determine which traces that can be replayed by the original model. Thus, the paper aim is to provide a technique for process models discovery which is as good in balancing between “overfitting” and “underfitting” as it is able to correctly classify the traces that can be replayed (i.e allowed) or non-replayable (disallowed) by the model. In other words, the study shows through the Fuzzy-BPMN replaying notation and the series of validation experiments - how given any classified trace (for the test events log) and discovered process model (the training log) it can be unambiguously determined whether or not the traces found can be replayed on the discovered model.

Ontology permits the addition of semantics to process models derived from mining the various data stored in many information systems. The ontological schema enables for automated querying and inference of useful knowledge from the different domain processes. Indeed, such conceptualization methods particularly ontologies for process management which is currently allied to semantic process mining trails to combine process models with ontologies, and are increasingly gaining attention in recent years. In view of that, this chapter introduces an ontology-based mining approach that makes use of concepts within the extracted event logs about domain processes to propose a method which allows for effective querying and improved analysis of the resulting models through semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner). The proposed method is a semantic-based process mining approach that is able to induce new knowledge based on previously unobserved behaviours, and a more intuitive and easy way to represent and query the datasets and the discovered models compared to other standard logical procedures. To this end, the study claims that it is possible to apply effective reasoning methods to make inferences over a process knowledge-base (e.g. the learning process) that leads to automated discovery of learning patterns and/or behaviour.