Text to 3D Scene Generation with Rich Lexical Grounding

The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics. However, prior work on the text to 3D scene generation task has used manually specified object categories and language that identifies them. We introduce a dataset of 3D scenes annotated with natural language descriptions and learn from this data how to ground textual descriptions to physical objects. Our method successfully grounds a variety of lexical terms to concrete referents, and we show quantitatively that our method improves 3D scene generation over previous work using purely rule-based methods. We evaluate the fidelity and plausibility of 3D scenes generated with our grounding approach through human judgments. To ease evaluation on this task, we also introduce an automated metric that strongly correlates with human judgments.Comment: 10 pages, 7 figures, 3 tables. To appear in ACL-IJCNLP 201

ALLOCATING RESOURCES: A CASE STUDY OF EARLY EDUCATION IN ZHEJIANG

Educational resources are varied by regions in China and early education has no exception, whereas student-teacher ratios, school facilities, teaching quality, and school curricula differed. Researching how educational resources are allocated could potentially improve the efficiency and equity of student learning despite regional differences. Zhejiang Province ranged from metropolitan cities such as Hangzhou to small cities and towns in rural areas, which provides diverse educational contexts to study this issue. Historically, Zhejiang has been a model for providing equitable student access and decreasing the cost of early education in China. However, the province has not provided solutions in addressing the equitable resources among various early education institutes. This study examines the differences in allocating educational resources by comparing the selected counties in Zhejiang. We will first employ the rough set theory to filter out the factors that might result from regional differences. In doing so, we could eliminate the possibility of multicollinearity and how it might affect the causal relationship in our regression model. Our initial findings reveal that the quality of teachers, the number of full-time teachers and staff, and the county’s economic index had various effects on teaching and learning. The study may provide a solution in addressing similar issues in other early education settings. The results suggest strategic planning for allocating early educational resources equitably and efficiently

Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View

We present Im2Pano3D, a convolutional neural network that generates a dense prediction of 3D structure and a probability distribution of semantic labels for a full 360 panoramic view of an indoor scene when given only a partial observation (<= 50%) in the form of an RGB-D image. To make this possible, Im2Pano3D leverages strong contextual priors learned from large-scale synthetic and real-world indoor scenes. To ease the prediction of 3D structure, we propose to parameterize 3D surfaces with their plane equations and train the model to predict these parameters directly. To provide meaningful training supervision, we use multiple loss functions that consider both pixel level accuracy and global context consistency. Experiments demon- strate that Im2Pano3D is able to predict the semantics and 3D structure of the unobserved scene with more than 56% pixel accuracy and less than 0.52m average distance error, which is significantly better than alternative approaches.Comment: Video summary: https://youtu.be/Au3GmktK-S

Exploring Concurrent Relationships between Economic Factors and Student Mobility in Expanding Higher Education Achieving 2030

Student mobility is one of the most important indicators to reflect institutional internationalization in a sustainable higher education system. Student mobility issues have been addressed in previous studies, and the phenomenon was discussed in association with related factors persistently. Since higher education sustainable development has received much scholarly attention, monitoring student mobility flows to adjust international strategies is necessary. This study explored practical approaches to detect student mobility flows in the process of higher education expansion. Targeting Taiwan’s higher education system as an example, we addressed the topic of system expansion and the core issues of student mobility. Target series data were collected from 1950 to 2021, including the economic growth ratio, GDP per capita, higher education enrollment, gross enrollment ratio (GER), and the number of inbound and outbound students. The data were transformed with index formats, for example, the economic growth ratio, enrollment increasing ratio (IR), and net flow ratio. The cross-correlation function (CCF) and autoregressive integrated moving average (ARIMA) were used to determine the correlations of the series data and their future trends. The findings suggested that the system expansion, with GER and IR, might reflect fluctuated student mobility in economic growth. This study confirmed that the time series approaches work well in detecting the phenomena of higher education expansion and their effects on student mobility flow in the future

ACADEMIC OUTCOMES OF UNDERGRADUATES LEARNING AT THE AGE OF COVID-19 PANDEMIC

Starting the year 2020, COVID-19 has become a global epidemic affecting 188 countries worldwide. As of October 10, 2020, there are 37,046,590 cases globally and 7,702,783 in the United States. COVID-19 has changed how universities operate, how teachers teach, and how students learn. Although many studies are exploring how teaching and learning operate in higher education institutions, little research has examined how COVID-19 impacted students’ academic outcomes at higher education institutions. This study explores how COVID-19 impacted learning among a student cohort enrolled in several sections of a yearlong course taught by the same instructors at the same university. Tableau is used to mine and analyze data as well as report results. Accounting for both demographic and language backgrounds data distinguishes differences in the impacts of the COVID-19 pandemic within a diverse student population. Once we recognize who bore the greatest burden of COVID’s impact, we can address the needs revealed

Detecting Female Students Transforming Entrepreneurial Competency, Mindset, and Intention into Sustainable Entrepreneurship

Entrepreneurship has been viewed as an opportunity for economic development and changing economic scenario in global markets. Women are viewed as a reservoir of entrepreneurial talents, so they can be growth engines in novel markets. Previous studies have considered entrepreneurship as the most effective way towards the economic empowerment of women. Female students engaged in entrepreneurial education have been addressed persistently, while what transforms them in an education process is still unclear. Considering the transforming global economy and its influence on higher education, this study aims to detect female students transforming entrepreneurial competency, mindset, and intention into sustainable entrepreneurship. Using a self-compiled survey, we targeted 752 female students to investigate their entrepreneurial competency, mindset, and intention. SPSS and AMOS were used to transform the data for interpretation. We assumed that the impact of female student’s entrepreneurial competency could be modified by an entrepreneurial mindset and result in entrepreneurial intention. To detect this causal relationship, this study employed reliability, factor, structural equation modeling (SEM), and bootstrapping analyses to verify the evidence. The result of the SEM confirms that the female students’ entrepreneurial competency will, through their entrepreneurial mindset, impact entrepreneurial intention. With bootstrapping, 5000 samples were collected, and it was demonstrated that the measure constructs were still reliable in the model. This study found that there is a mediation effect between entrepreneurial competency and entrepreneurial intention. The entrepreneurial mindset plays a crucial role in the transformation process. Without an entrepreneurial mindset, entrepreneurial competency cannot exert a significant effect on entrepreneurial intention. The findings can help reinvent related entrepreneurial education in higher education