The Role of Delivery Methods on the Perceived Learning Performance and Satisfaction of IT Students in Software Programming Courses

More and more information technology (IT) programs are offering distance learning courses to their students. However, to date, there are a very limited number of published articles in the IT education literature that compare how different methods of delivering distance course relate to undergraduate students’ learning outcomes in IT software programming courses taught by the same instructor. Thus, we conducted a case study to assess the predictive relationships between distance course delivery method (face-to-face, satellite broadcasting, and live video-streaming) and students’ perceived learning performance and satisfaction in IT software programming courses taught by the same instructor. The results suggested that the choice of delivery method was related to students’ satisfaction and programming skill enhancement. However, we did not find a relationship between the delivery method and the students’ perceived learning performance. Specifically, the participants in the face-to-face delivery method group were more likely to feel satisfied with the delivery method than the students using the other two delivery methods (i.e., satellite broadcasting and live video streaming)

Detecting Galaxy-Filament Alignments in the Sloan Digital Sky Survey III

Previous studies have shown the filamentary structures in the cosmic web influence the alignments of nearby galaxies. We study this effect in the LOWZ sample of the Sloan Digital Sky Survey using the "Cosmic Web Reconstruction" filament catalogue. We find that LOWZ galaxies exhibit a small but statistically significant alignment in the direction parallel to the orientation of nearby filaments. This effect is detectable even in the absence of nearby galaxy clusters, which suggests it is an effect from the matter distribution in the filament. A nonparametric regression model suggests that the alignment effect with filaments extends over separations of 30-40 Mpc. We find that galaxies that are bright and early-forming align more strongly with the directions of nearby filaments than those that are faint and late-forming; however, trends with stellar mass are less statistically significant, within the narrow range of stellar mass of this sample.Comment: 14 pages, 13 figures. Accepted to the MNRA

Estradiol regulates miR-135b and mismatch repair gene expressions via estrogen receptor-β in colorectal cells.

Estrogen has anti-colorectal cancer effects which are thought to be mediated by mismatch repair gene (MMR) activity. Estrogen receptor (ER) expression is associated with microRNA (miRNA) expression in ER-positive tumors. However, studies of direct link between estrogen (especially estradiol E2), miRNA expression, and MMR in colorectal cancer (CRC) have not been done. In this study, we first evaluated the effects of estradiol (E2) and its antagonist ICI182,780 on the expression of miRNAs (miR-31, miR-155 and miR-135b) using COLO205, SW480 and MCF-7 cell lines, followed by examining the association of tissue miRNA expression and serum E2 levels using samples collected from 18 colorectal cancer patients. E2 inhibited the expressions of miRNAs in COLO205 cells, which could be reversed by E2 antagonist ICI 182.780. The expression of miR-135b was inversely correlated with serum E2 level and ER-β mRNA expression in CRC patients' cancer tissues. There were significant correlations between serum E2 level and expression of ER-β, miR-135b, and MMR in colon cancer tissue. This study suggests that the effects of estrogen on MMR function may be related to regulating miRNA expression via ER-β, which may be the basis for the anti-cancer effect in colorectal cells

Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade

As higher education diversifies its delivery modes, our ability to use the predictive and analytical power of educational data mining (EDM) to understand students\u27 learning experiences is a critical step forward. The adoption of EDM by higher education as an analytical and decision making tool is offering new opportunities to exploit the untapped data generated by various student information systems (SIS) and learning management systems (LMS). This paper describes a hybrid approach which uses EDM and regression analysis to analyse live video streaming (LVS) students\u27 online learning behaviours and their performance in their courses. Students\u27 participation and login frequency, as well as the number of chat messages and questions that they submit to their instructors, were analysed, along with students\u27 final grades. Results of the study show a considerable variability in students\u27 questions and chat messages. Unlike previous studies, this study suggests no correlation between students\u27 number of questions/chat messages/login times and students\u27 success. However, our case study reveals that combining EDM with traditional statistical analysis provides a strong and coherent analytical framework capable of enabling a deeper and richer understanding of students\u27 learning behaviours and experiences

Compact Floor-Planning via Orderly Spanning Trees

Floor-planning is a fundamental step in VLSI chip design. Based upon the concept of orderly spanning trees, we present a simple O(n)-time algorithm to construct a floor-plan for any n-node plane triangulation. In comparison with previous floor-planning algorithms in the literature, our solution is not only simpler in the algorithm itself, but also produces floor-plans which require fewer module types. An equally important aspect of our new algorithm lies in its ability to fit the floor-plan area in a rectangle of size (n-1)x(2n+1)/3. Lower bounds on the worst-case area for floor-planning any plane triangulation are also provided in the paper.Comment: 13 pages, 5 figures, An early version of this work was presented at 9th International Symposium on Graph Drawing (GD 2001), Vienna, Austria, September 2001. Accepted to Journal of Algorithms, 200

A Novel Structure for Double Negative NIMs towards UV Spectrum with High FOM

A novel ring structure is proposed for double negative NIMs at visible light spectrum with high FOM (e.g. about 11 at a wavelength of 583 nm) and low loss. Besides the effective medium theory, an equivalent circuit model is also given to explain physically why our novel structure can give double negative behavior with low loss. Adapted from the original ring structure, two other types of structures, namely, disk and nanowire structures, are also given to further push double negative NIMs toward ultraviolet (UV) spectrum

Optical music recognition of the singer using formant frequency estimation of vocal fold vibration and lip motion with interpolated GMM classifiers

The main work of this paper is to identify the musical genres of the singer by performing the optical detection of lip motion. Recently, optical music recognition has attracted much attention. Optical music recognition in this study is a type of automatic techniques in information engineering, which can be used to determine the musical style of the singer. This paper proposes a method for optical music recognition where acoustic formant analysis of both vocal fold vibration and lip motion are employed with interpolated Gaussian mixture model (GMM) estimation to perform musical genre classification of the singer. The developed approach for such classification application is called GMM-Formant. Since humming and voiced speech sounds cause periodic vibrations of the vocal folds and then the corresponding motion of the lip, the proposed GMM-Formant firstly operates to acquire the required formant information. Formant information is important acoustic feature data for recognition classification. The proposed GMM-Formant method then uses linear interpolation for combining GMM likelihood estimates and formant evaluation results appropriately. GMM-Formant will effectively adjust the estimated formant feature evaluation outcomes by referring to certain degree of the likelihood score derived from GMM calculations. The superiority and effectiveness of presented GMM-Formant are demonstrated by a series of experiments on musical genre classification of the singer

Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks

Adapting large-scale pretrained models to various downstream tasks via fine-tuning is a standard method in machine learning. Recently, parameter-efficient fine-tuning methods show promise in adapting a pretrained model to different tasks while training only a few parameters. Despite their success, most existing methods are proposed in Natural Language Processing tasks with language Transformers, and adaptation to Computer Vision tasks with Vision Transformers remains under-explored, especially for dense vision tasks. Further, in multi-task settings, individually fine-tuning and storing separate models for different tasks is inefficient. In this work, we provide an extensive multi-task parameter-efficient benchmark and examine existing parameter-efficient fine-tuning NLP methods for vision tasks. Our results on four different dense vision tasks showed that existing methods cannot be efficiently integrated due to the hierarchical nature of the Hierarchical Vision Transformers. To overcome this issue, we propose Polyhistor and Polyhistor-Lite, consisting of Decomposed HyperNetworks and Layer-wise Scaling Kernels, to share information across different tasks with a few trainable parameters. This leads to favorable performance improvements against existing parameter-efficient methods while using fewer trainable parameters. Specifically, Polyhistor achieves competitive accuracy compared to the state-of-the-art while only using ~10% of their trainable parameters. Furthermore, our methods show larger performance gains when large networks and more pretraining data are used.Comment: Accepted to NeurIPS 2022; Project Page is at https://ycliu93.github.io/projects/polyhistor.htm