DFSeer: A visual analytics approach to facilitate model selection for demand forecasting

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.Comment: 10 pages, 5 figures, ACM CHI 202

Is automatic speech recognition ready for non-native speech? A data collection effort and initial experiments in modelling conversational Hispanic English

We describe the protocol used for collecting a corpus of conversational English speech from non-native speakers at several levels of proficiency, and report the results of preliminary automatic speech recognition (ASR) experiments on this corpus using HTK-based ASR systems. The speech corpus contains both read and conversational speech recorded simultaneously on wide-band and telephone channels, and has detailed time aligned transcriptions. The immediate goal of the ASR experiments is to assess the difficulty of the ASR problem in language learning exercises and thus to gauge how current ASR technology may be used in conversational computer assisted language learning (CALL) systems. The long-term goal of this research, of which the data collection and experiments are a first step, is to incorporate ASR into computer-based conversational language instruction systems.

Constraints and Development in Children's Block Construction

Block construction tasks are highly complex, yet even young children engage in these tasks in both informal and formal learning settings. In this paper, we ask whether the specific paths through which children build a structure are unique to the individual, or alternatively, constrained by similar principles across individuals and over age. Our results show that although children between 4 and 8 make frequent errors in copying model constructions, there is a striking amount of consistency in specific attributes of their paths of construction, and this consistency mirrors that of adults. The build paths suggest that although children sometimes build inefficiently, they tend to build layer-by-layer, consistent with a role for intuitive physics that enables the creation of stable structures