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An increasingly global environment expects graduating Engineering students to perform, live and work across cultures. Most intercultural competence research and associated global engineering education is focused on developing the global engineering skill set through long-term travel experiences such as study abroad programs. These programs can be expensive from both a time and money standpoint, limiting the participation to more privileged members of a community, and are not scalable to support broader participation. This work-in-progress addresses this research gap by focusing on the development of the students’ global learner mindset without requiring extensive travel. The project will investigate four different global engagement interventions, including the use of engineering case studies, the intentional formation of multi-national student teams, a Collaborative Online International Learning (COIL) research project, and a community engaged project within a short course. These interventions can be used to develop a holistic global learner mindset and global engineering education approach to foster global competence in undergraduate engineering students. The four global engagement interventions will be grounded in the global engineering competency (GEC) theoretical framework and assessed for their ability to foster a global learner mindset in engineering students. A mixed-methods approach will be used to assess students’ global learner mindset and skill set. This research will use the Global Engagement Survey (GES), the Global Engineering Competency Scale (GECS) and specific questions developed by the researchers to evaluate improvements in the participating students’ global engineering skill set and answer specific research questions including: 1) To what extent can global competence be developed in engineering students through the use of the proposed global engagement interventions; and 2) what are the relative strengths of each of the proposed global engagement interventions in developing global engineering competence? Combined, these research measures will provide both an accurate picture of how each global engagement intervention impacts the formation of a global learner mindset in engineering education, and also its associated ability to develop and/or improve global engineering skills. The outcomes of this study will generate valuable knowledge to understand how each global engagement intervention impacts the formation of global engineering competence. In this work-in-progress study, the authors discuss the four global engagement interventions with specific learning objectives that have been mapped to the overall student outcomes for the project. These objectives have also been mapped to the GES and GECS instruments. Finally the faculty members have developed qualitative tools to augment the GES and GECS to identify the global engineering skill sets each intervention is generating. This paper lays the foundation before implementing the interventions and performing their associated assessments over the several subsequent semesters. 

Accurate detection of abnormal behavior can help improve public safety. In this work, a 3D convolutional neural network (CNN) is implemented to detect violence captured by surveillance cameras. A comprehensive study of model hyper-parameter tuning is addressed to show competitive violence detection results using a general action recognition CNN without modifying the original architecture. Experimental results on three publicly available benchmark datasets show that the proposed method outperforms other sophisticated techniques designed specifically to detect violence in videos. Our analysis further indicates that reasonable network parameter adjustments can be an effective mechanism to guide the design of computer vision models in abnormal human behavior detection. 

When people interact, aspects of their speech and language patterns often converge in inter- actions involving one or more languages. Most studies of speech convergence in conversations have examined monolingual interactions, whereas most studies of bilingual speech conver- gence have examined spoken responses to prompts. However, it is not uncommon in multi- lingual communities to converse in two languages, where each speaker primarily produces only one of the two languages. The present study examined complexity matching and lexical matching as two measures of speech convergence in conversations spoken in English, Spanish, or both languages. Complexity matching measured convergence in the hierarchical timing of speech, and lexical matching measured convergence in the frequency distributions of lemmas produced. Both types of matching were found equally in all three language conditions. Taken together, the results indicate that convergence is robust to monolingual and bilingual interac- tions because it stems from basic mechanisms of coordination and communication.