Community detection is an important research issue in complex network mining. In this paper, firstly, we define central nodes, called Extended Local Max-Degree ELMD nodes in a complex network. All the central nodes are used for the community expanding. We also prove that ELMD method is more precise and dispersed than local max-degree method in the real datasets. Secondly, we propose an improved local expansion method to expand community from the seeds (ELMD nodes), and this process is named as Extended Local Community Expansion with Modified R method (ELCEMR). ELCEMR is an unsupervised learning method, and does not need any priori-knowledge. Finally, the validations against the real-world datasets show that the proposed method performs better than other algorithms for community detection.

The process of human annotation of sensor data is at the base of research areas such as participatory sensing and mobile crowdsensing. While much research has been devoted to assessing the quality of sensor data, the same cannot be said about annotations, which are fundamental to obtain a clear understanding of users experience. We present an evaluation of an interdisciplinary annotation methodology allowing users to continuously annotate their everyday life. The evaluation is done on a dataset from a project focused on the behaviour of students and how this impacts on their academic performance. We focus on those annotations concerning locations and movements of students, and we evaluate the annotations quality by checking their consistency. Results show that students are highly consistent with respect to the random baseline, and that these results can be improved by exploiting the semantics of annotations.

Mobile Crowd Sensing (MCS) is a novel IoT paradigm where sensor data, as collected by the user’s mobile devices, are integrated with user-generated content, e.g., annotations, self-reports, or images. While providing many advantages, the human involvement also brings big challenges, where the most critical is possibly the poor quality of human-provided content, most often due to the inaccurate input from non-expert users. In this paper, we propose Skeptical Learning, an interactive machine learning algorithm where the machine checks the quality of the user feedback and tries to fix it when a problem arises. In this context, the user feedback consists of answers to machine generated questions, at times defined by the machine. The main idea is to integrate three core elements, which are (i) sensor data , (ii) user answers, and (iii) existing prior knowledge of the world, and to enable a second round of validation with the user any time these three types of information jointly generate an inconsistency. The proposed solution is evaluated in a project focusing on a university student life scenario. The main goal of the project is to recognize the locations and transportation modes of the students. The results highlight an unexpectedly high pervasiveness of user mistakes in the university students life project. The results also shows the advantages provided by Skeptical Learning in dealing with the mislabeling issues in an interactive way and improving the prediction performance.