Flexible document organization: comparing fuzzy and possibilistic approaches

System flexibility means the ability of a system to manage imprecise and/or uncertain information. A lot of commercially available Information Retrieval Systems (IRS) address this issue at the level of query formulation. Another way to make the flexibility of an IRS possible is by means of the flexible organization of documents. Such organization can be carried out using clustering algorithms by which documents can be automatically organized in multiple clusters simultaneously. Fuzzy and possibilistic clustering algorithms are examples of methods by which documents can belong to more than one cluster simultaneously with different membership degrees. The interpretation of these membership degrees can be used to quantify the compatibility of a document with a particular topic. The topics are represented by clusters and the clusters are identified by one or more descriptors extracted by a proposed method. We aim to investigate if the performance of each clustering algorithm can affect the extraction of meaningful overlapping cluster descriptors. Experiments were carried using well-known collections of documents and the predictive power of the descriptors extracted from both fuzzy and possibilistic document clustering was evaluated. The results prove that descriptors extracted after both fuzzy and possibilistic clustering are effective and can improve the flexible organization of documents.CAPES (Coordination for the Improvement of Higher Level Personnel) (PDSE grant 5983-11-8)FAPESP (Sao Paulo Research Foundation) (grant 2011/19850-9

A Cloud-Edge-aided Incremental High-order Possibilistic c-Means Algorithm for Medical Data Clustering

Medical Internet of Things are generating a big volume of data to enable smart medicine that tries to offer computer-aided medical and healthcare services with artificial intelligence techniques like deep learning and clustering. However, it is a challenging issue for deep learning and clustering algorithms to analyze large medical data because of their high computational complexity, thus hindering the progress of smart medicine. In this paper, we present an incremental high-order possibilistic c-means algorithm on a cloud-edge computing system to achieve medical data co-clustering of multiple hospitals in different locations. Specifically, each hospital employs the deep computation model to learn a feature tensor of each medical data object on the local edge computing system and then uploads the feature tensors to the cloud computing platform. The high-order possibilistic c-means algorithm (HoPCM) is performed on the cloud system for medical data clustering on uploaded feature tensors. Once the new medical data feature tensors are arriving at the cloud computing platform, the incremental high-order possibilistic c-means algorithm (IHoPCM) is performed on the combination of the new feature tensors and the previous clustering centers to obtain clustering results for the feature tensors received to date. In this way, repeated clustering on the previous feature tensors is avoided to improve the clustering efficiency. In the experiments, we compare different algorithms on two medical datasets regarding clustering accuracy and clustering efficiency. Results show that the presented IHoPCM method achieves great improvements over the compared algorithms in clustering accuracy and efficiency

Human Factors in Agile Software Development

Through our four years experiments on students' Scrum based agile software development (ASD) process, we have gained deep understanding into the human factors of agile methodology. We designed an agile project management tool - the HASE collaboration development platform to support more than 400 students self-organized into 80 teams to practice ASD. In this thesis, Based on our experiments, simulations and analysis, we contributed a series of solutions and insights in this researches, including 1) a Goal Net based method to enhance goal and requirement management for ASD process, 2) a novel Simple Multi-Agent Real-Time (SMART) approach to enhance intelligent task allocation for ASD process, 3) a Fuzzy Cognitive Maps (FCMs) based method to enhance emotion and morale management for ASD process, 4) the first large scale in-depth empirical insights on human factors in ASD process which have not yet been well studied by existing research, and 5) the first to identify ASD process as a human-computation system that exploit human efforts to perform tasks that computers are not good at solving. On the other hand, computers can assist human decision making in the ASD process.Comment: Book Draf