AI-Based Collaborative Teaching: Strategies and Analysis in Visual Communication Design

With the rapid development of technology, AI has been widely applied in multiple fields, especially the field of education. As a discipline involving art, technology and creativity, visual communication design is facing the challenge of keeping up with the times and combining new technologies for innovation. Collaborative teaching model emphasizes multi-party participation and collaborative learning, and its proposal has injected new vitality into traditional educational patterns. However, existing studies, which combine collaborative teaching model with artificial intelligence, still have limitations in application and practice, and most of them remain in the theoretical discussion stage and lack empirical support. This study aimed to make up for this deficiency. After in-depth analysis of educational data, a forecasting model of collaborative teaching demand based on AI was proposed. Course content suitable for the collaborative teaching model was further planned for the education in visual communication design

CGPE: A user-friendly gene and pathway explore webserver for public cancer transcriptional data

Digitized for IUPUI ScholarWorks inclusion in 2021.High throughput technology has been widely used by researchers to understand diseases at the molecular level. Database and servers for downloading and analyzing these publicly data is available as well. But there is still lacking tools for facilitating researchers to study the function of genes in pathways views by integrated public omics data

Develop the Disease Specific Bioinformatics Platforms with Integrated Bioinformatics Data

Indiana University-Purdue University Indianapolis (IUPUI)With the advance of multiple types of omics technology and corresponding analytical methods, various type of bioinformatic data have become available. Mining and integrating these data for analysis will provide valuable insights for disease mechanism investigation, drug target identification and new drug development. However, most of the omics data are large size, heterogeneous, and complex, it is challenging for biomedical researchers to mine the data for relevant evidence, especially for those with limited computational skills. In this thesis, I aimed to develop disease specific platforms integrated with multimodal bioinformatic data types to provide researchers with strong bioinformatics support. To achieve this goal, I explored advanced transcriptomic data analytical methods and proposed a novel biomarker for the prediction of overall survival of colon cancer patients, then prototyped a user-friendly patient oriented clinical decision support system to provide accurate and intuitive colorectal cancer risk factor assessment. With the experience of the transcriptomic data analytical methods and the web-based application development, I further designed and implemented Cancer Gene and Pathway Explorer which is an integrative bioinformatics webserver that can be used for cancer publication trends investigation, gene set enrichment analysis with integrated data, and optimal cancer cell line identification. Based on the framework of CGPE, I developed another bioinformatics platform focusing on Alzheimer’s disease, called Alzheimer’s Disease Explorer, which is a first-of-its-kind bioinformatics server, providing rich bioinformatic support from literature, omics and chemical data to facilitate researchers in ND drug development field. By accomplishing a series of work in my thesis, I have shown that integrated disease specific bioinformatics platforms can provide great value to the research community by allowing 1.) fast and accurate investigation of currently available literature, 2.) quick hypothesis generation and validation using transcriptomic datasets, 3.) multi-dimension drug target evaluation and 4) fast querying of published bioinformatics outcomes

Visual diagnosis of tree boosting methods

Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms

Miscible density driven convective mass transfer process analysis based on Entransy dissipation theory

Density driven convective mass transfer process in porous media is one of the most universal phenomena in underground aquifer. In this study, an original model defining Nu (or Sh) number for miscible mass transfer system was derived, based on basic concept of integrated entransy dissipation rate. Numerical simulation results of density driven convective mass transfer process in a closed Hele-Shaw cell and porous media are analyzed. In the process of dilute brine-water mass transfer system in Hele-Shaw cell, three different stages were observed. Meanwhile, time dependent entransy variation and Nu number using our definition also show three different steps in accordance with the observing phenomenon which are perturbation growing stage, instable mass transfer stage and stabilized stage. Very different fingering patterns were observed in dilute brine-water system and PEG-Water system because the latter one has not only the Non-Monotonic Density-Concentration profile but also the strong dependence of viscosity on concentration which can cause viscous-instability accompanied with density driven instability

ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models

Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates). Recent end-to-end neural methods thus require substantial training examples to learn to disambiguate and describe the data. Yet, real-world data-to-text problems often suffer from various data-scarce issues: one may have access to only a handful of or no training examples, and/or have to rely on examples in a different domain or schema. To fill this gap, we propose Any-Shot Data-to-Text (ASDOT), a new approach flexibly applicable to diverse settings by making efficient use of any given (or no) examples. ASDOT consists of two steps, data disambiguation and sentence fusion, both of which are amenable to be solved with off-the-shelf pretrained language models (LMs) with optional finetuning. In the data disambiguation stage, we employ the prompted GPT-3 model to understand possibly ambiguous triples from the input data and convert each into a short sentence with reduced ambiguity. The sentence fusion stage then uses an LM like T5 to fuse all the resulting sentences into a coherent paragraph as the final description. We evaluate extensively on various datasets in different scenarios, including the zero-/few-/full-shot settings, and generalization to unseen predicates and out-of-domain data. Experimental results show that ASDOT consistently achieves significant improvement over baselines, e.g., a 30.81 BLEU gain on the DART dataset under the zero-shot setting.Comment: Findings of EMNLP 202