This book synthesizes the work of the managing requirements knowledge (MARK) community during the last 5 years. The first idea to organize a workshop on this topic came to our minds in winter 2007. We were both working on our Ph.D. projects at the Technische Universität München (TUM) under the supervision of Bernd Brügge. Anil was focusing on software product lines, while Walid was looking at the application of ontologies and machine learning to collaborative software engineering, in particular during bug fixing and API reuse. Our fields of interest seemed divergent at first glance. However, after a couple of discussionsalso with colleagues from industry-we found that some of the problems we were trying to address are very similar. Valuable experiences and knowledge gained in the course of software projects, in particular during the work with requirements, remain tacit in the mind of people. The same problems in understanding and implementing requirements occur again and again. We were convinced about the need for a new perspective on requirementsconsidering them as a knowledge asset in software organizations-in addition to the engineering and lifecycle perspectives. We were convinced about the huge potentials of recent trends such as ontologies, wikis, Web 2.0, recommendation systems, and data mining, to the requirements engineering community. In the last years, the MARK workshop successfully took place in Barcelona, Atlanta, Sydney, and Trento. It has been one of the most successful workshops at the IEEE International Conference on Requirements Engineering that is based on submission and registration statistics, as well as the feedback of the participants. The achievements are remarkable. Novel approaches such as "recommending features and stakeholders by analyzing requirements repositories" or "using semantic wikis to represent and reason about requirements" have found their way to main conferences and journal in the field. Some of the tools are already being used in practice. v
Interactions via social media platforms have made it possible for anyone, irrespective of physical location, to gain access to quick information on events taking place all over the globe. However, the semantic processing of social media data is complicated due to challenges such as language complexity, unstructured data, and ambiguity. In this paper, we proposed the Social Media Analysis Framework for Event Detection (SMAFED). SMAFED aims to facilitate improved semantic analysis of noisy terms in social media streams, improved representation/embedding of social media stream content, and improved summarization of event clusters in social media streams. For this, we employed key concepts such as integrated knowledge base, resolving ambiguity, semantic representation of social media streams, and Semantic Histogram-based Incremental Clustering based on semantic relatedness. Two evaluation experiments were conducted to validate the approach. First, we evaluated the impact of the data enrichment layer of SMAFED. We found that SMAFED outperformed other pre-processing frameworks with a lower loss function of 0.15 on the first dataset and 0.05 on the second dataset. Second, we determined the accuracy of SMAFED at detecting events from social media streams. The result of this second experiment showed that SMAFED outperformed existing event detection approaches with better Precision (0.922), Recall (0.793), and F-Measure (0.853) metric scores. The findings of the study present SMAFED as a more efficient approach to event detection in social media.
Evidence from the literature suggests that Game-based Learning (GBL) can help students learn better. A gamified environment can provide a blend of serious learning and fun for students. Some researchers have observed that GBL could stimulate valuable educational outcomes and positively impact a child's life. However, evidence shows that students in poor communities in South Africa are performing poorly academically due to poor student engagement and lack of motivation. Although GBL platforms are being used widely in some developed countries, they have not been widely adopted in South African schools. This paper provides insight on the preferences of learners in South African schools with respect to GBL. We conducted a survey involving participants from four South African Schools (2 Primary schools and 2 Secondary schools) to determine the type and mode of GBL that Grade R-12 learners prefer. A total of 193 learners participated in the survey. The study found the learners' preferential order of type of games are puzzles, video games, simulation games, word games, and card games. The aspects of visual aesthetics, musical scores, and incentive appeal to most learners. At the same time, there is also a preference for games that involves a challenge, enable competition with peers, and promotes curiosity. Based on our findings, we argue that multiplayer game platforms that have rich social interaction features would suit learners in South African schools, while single-player game platforms that can stimulate logical thinking and reasoning will also be helpful to aid learners in identified difficult subjects like Mathematics, Mathematical Literacy, Pure Science, accounting, and Geography. The study provides a solid foundation for understanding the requirements for developing GBL solutions to support education in South Africa. Furthermore, the study's findings could guide government policy on the adoption of GBL and software developers in making design choices during the development of GBL platforms.
Accurate treatment decision-making for disease treatment is important, particularly if it is done in a way that helps to overcome the challenges associated with low resource setting. This include shortage of qualified personnel, infrastructure, ready access to devices, and healthcare by common people. Although real-time gait analysis has been used for the diagnosis of diseases that are associated with gait impairments, the need to improve on the performance of gait-based prediction by augmenting it with other sources of knowledge in a complementary way has been acknowledged. In contrast, to existing approaches, this paper presents the design of a conceptual framework that will enable the semantic integration and use of information from multiple health data sources in order to ensure efficient treatment decision making for gait-related diseases in a low resource setting such as South Africa. The analytical evaluation of the proposed framework suggests that it has sufficient merit and potential to be usable and useful in low-resource settings.
Direct marketing enables businesses to identify customers that could be interested in product offerings based on historical customer transactions data. Several machine learning (ML) tools are currently being used for direct marketing. However, the disadvantage of ML algorithmic models is that even though results could be accurate, they lack relevant explanations. The lack of detailed explanations that justify recommendations has led to reduced trust in ML-based recommendations for decision making in some critical real-world domains. The telecommunication domain has continued to witness a decline of revenue in core areas such as voice and text messaging services which make direct marketing useful to increase profit. This paper presents the conceptual design of a machine learning process framework that will enable telecom subscribers that should be targeted for direct marketing of new products to be identified, and also provide explanations for the recommendations. To do this, a hybrid framework that employs supervised learning, case-based reasoning and rule-based reasoning is proposed. The operational workflow of the framework is demonstrated with an example, while the plan of implementation and evaluation are also discussed.
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