@article{PhiriMeinelSuleman2016,
  author    = {Phiri, Lighton and Meinel, Christoph and Suleman, Hussein},
  title     = {Streamlined orchestration: An orchestration workbench framework for effective teaching},
  series = {Current opinion in plant biology},
  volume    = {95},
  journal   = {Current opinion in plant biology},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0360-1315},
  doi       = {10.1016/j.compedu.2016.01.011},
  pages     = {231 -- 238},
  year      = {2016},
  abstract  = {Effective classroom management is considered a key criterion to making classrooms effective learning environments. Supporting classroom orchestration—the teacher-centric real-time management of classroom activities—is central to achieving effective classroom management. However, the multi-faceted nature of classroom orchestration, its complexity, and general classroom constraints such as time, present challenges for the effective management of the modern-day classroom environment. Though effective, most existing approaches for overcoming orchestration challenges, such as Google Classroom, are arguably ad hoc. We argue that streamlined technology-driven orchestration can be attained through the use of an orchestration workbench, potentially making educators more effective within formal learning environments. Early supporting evidence, from a study involving the use of a prototype orchestration tool, demonstrates the feasibility of organised orchestration and its potential to improve students' learning experience.},
  language  = {en}
}
@article{WangYangMeinel2016,
  author    = {Wang, Cheng and Yang, Haojin and Meinel, Christoph},
  title     = {A deep semantic framework for multimodal representation learning},
  series = {Multimedia tools and applications : an international journal},
  volume    = {75},
  journal   = {Multimedia tools and applications : an international journal},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1380-7501},
  doi       = {10.1007/s11042-016-3380-8},
  pages     = {9255 -- 9276},
  year      = {2016},
  abstract  = {Multimodal representation learning has gained increasing importance in various real-world multimedia applications. Most previous approaches focused on exploring inter-modal correlation by learning a common or intermediate space in a conventional way, e.g. Canonical Correlation Analysis (CCA). These works neglected the exploration of fusing multiple modalities at higher semantic level. In this paper, inspired by the success of deep networks in multimedia computing, we propose a novel unified deep neural framework for multimodal representation learning. To capture the high-level semantic correlations across modalities, we adopted deep learning feature as image representation and topic feature as text representation respectively. In joint model learning, a 5-layer neural network is designed and enforced with a supervised pre-training in the first 3 layers for intra-modal regularization. The extensive experiments on benchmark Wikipedia and MIR Flickr 25K datasets show that our approach achieves state-of-the-art results compare to both shallow and deep models in multimodal and cross-modal retrieval.},
  language  = {en}
}