"It Puts us in our Students' Shoes": Listening to Voices from Teacher Candidates on Their Test-Taking Experience

This study explores teacher candidates' experiential learning through their test-taking experiences while attending a Bachelor of Education (B. Ed.) program. Eighty-four written reflections by teacher candidates taking a mid-term course examination on classroom assessment practices were analyzed. Major themes emerging from these reflections on the test-taking experience are related to validity concepts of construct representation, construct-irrelevant variance, relevance, and fairness. The study reveals that the test-taking experience could be valuable to teacher candidates in their learning of classroom assessment practices and in their understanding of the issues in test taking that may influence test performance. This, in turn, could potentially provide teacher candidates with a direct framework for their future classroom assessment practices, by which they may support their own future students

Constructive neural networks with applications to image compression and pattern recognition

The theory of Neural Networks (NNs) has witnessed a striking progress in the past fifteen years. The basic issues, such as determining the structure and size of the network, and developing efficient training/learning strategies have been extensively investigated. This thesis is mainly focused on constructive neural networks and their applications to regression, image compression and pattern recognition problems. The contributions of this work are as follows. First, two new strategies are proposed for a constructive One-Hidden-Layer Feedforward NN (OHL-FNN) that grows from a small initial network with a few hidden units to one that has sufficient number of hidden units as required by the underlying mapping problem. The first strategy denoted as error scaling is designed to improve the training efficiency and generalization performance of the OHL-FNN. The second strategy is a pruning criterion that produces a smaller network while not degrading the generalization capability of the network. Second, a novel strategy at the structure level adaptation is proposed for constructing, multi-hidden-layer FNNs. By utilizing the proposed scheme, a FNN is obtained that has sufficient number of hidden layers and hidden units that are required by the complexity of the mapping being considered. Third, a new constructive OHL-FNN at the functional level adaptation is developed. According to this scheme, each hidden unit uses a polynomial as its activation function that is different from those of the other units. This permits the growing network to employ different activation functions so that the network would be able to represent and capture the underlying map more efficiently as compared to the fixed activation function networks. Finally the proposed error scaling and input-side pruning techniques are applied to regression, still and moving image compression, and facial expression recognition problems. The proposed constructive algorithm for creating multilayer FNNs is applied to a range of regression problems. The proposed polynomial OHL-FNN is utilized to solve both regression and classification problems. It has been shown through extensive simulations that all the proposed techniques and networks produce very promising result

A Review of An Interactive Augmented Reality Customization Clothing System Using Finger Tracking Techniques as Input Device

This paper mainly focuses on the review of applications in Augmented Reality (AR) technology in the field of clothing customization using finger tracking techniques as input device. Review the influence and role of AR technology in the clothing customization industry. A comparative analysis of the technological advances and technical deficiencies embodied in the virtual fitting software developed by the world in the past 10 years using AR technology. Through research and comparison, a personalized clothing customization system based on AR technology is proposed. The system can enhance people’s experience and interaction with the fashion design process and improve the satisfaction of clothing customization using finger techniques as input device

Self-rating depression during early postoperative period after colostomy following radical surgery for rectal cancer

Introductions: Colorectal cancer (CRC) is 3rd most common cancer. Half of which requires colostomy. It leads to anxiety and depression with less than optimal quality of life. Zung Self-rating Depression Scale is a reliable tool used in Chinese population for identifying and addressing mental health status for appropriate education. The aim of this study is to investigate the depression state in rectal cancer patients after colostomy, then analyze its influence factors. Methods: A cross sectional study in rectal cancer patients who had colostomy after radical surgery for rectal cancer were investigated for depression during early postoperative period within one week using Zung’s self-rating depression scale (SDS). Multiple logistic regression analysis was done to identify the risk factor. Results: There were 55 colostomies patients (male 30 and female 25 patients, age 50.11+/-13.17 years) after rectal cancer surgery during the study period. The SDS score of was higher than national norm (P<0.01). The risk factors for depression were female gender, younger age, lower economic status, and lesser degree of understanding of the disease. Conclusions: The depression level of rectal cancer patients after colostomy was higher than normal population, especially in female, young age, with poor understanding of disease and lower economy status. The effective measures should be targeted to strengthen the health psychosocial health of these patients. Keywords: colorectal cancer, colostomy, early postoperative depression, Zung self-rating depression score SD

A TOPIC SENSITIVE SIMRANK (TSSR) MODEL FOR EXPERTS FINDING ON ONLINE RESEARCH SOCIAL PLATFORMS

As an efficient online academic information repository and information channel with crowds’ contribution, online research social platforms have become an efficient tool for various kinds of research & management applications. Social network platforms have also become a major source to seek for field experts. They have advantages of crowd contributions, easy to access without geographic restrictions and avoiding conflict of interests over traditional database and search engine based approaches. However, current research attempts to find experts based on features such as published research work, social relationships, and online behaviours (e.g. reads and downloads of publications) on social platforms, they ignore to verify the reliability of identified experts. To bridge this gap, this research proposes an innovative Topic Sensitive SimRank (TSSR) model to identify “real” experts on social network platforms. TSSR model includes three components: LDA for Expertise Extension, Topic Sensitive Network for Reputation Measurement, and Topic Sensitive SimRank for unsuitable experts detection. We also design a parallel computing strategy to improve the efficiency of the proposed methods. Last, to verify the effectiveness of the proposed model, we design an experiment on one of the research social platforms-ScholarMate to seek for experts for companies that need academic-industry collaboration

Measuring Semantic and Structural Information for Data Oriented Workflow Retrieval with Cost Constraints

The reuse of data oriented workflows (DOWs) can reduce the cost of workflow system development and control the risk of project failure and therefore is crucial for accelerating the automation of business processes. Reusing workflows can be achieved by measuring the similarity among candidate workflows and selecting the workflow satisfying requirements of users from them. However, due to DOWs being often developed based on an open, distributed, and heterogeneous environment, different users often can impose diverse cost constraints on data oriented workflows. This makes the reuse of DOWs challenging. There is no clear solution for retrieving DOWs with cost constraints. In this paper, we present a novel graph based model of DOWs with cost constraints, called constrained data oriented workflow (CDW), which can express cost constraints that users are often concerned about. An approach is proposed for retrieving CDWs, which seamlessly combines semantic and structural information of CDWs. A distance measure based on matrix theory is adopted to seamlessly combine semantic and structural similarities of CDWs for selecting and reusing them. Finally, the related experiments are made to show the effectiveness and efficiency of our approach

3D Point Cloud Data Registration Algorithm Based on Augmented Reality Technology

Point cloud data registration is one of the key steps in 3D laser scanning data processing. At present, point cloud data registration has the problems of error and is too much time-consuming. In order to solve the above problems, a 3D point cloud data registration algorithm based on augmented reality technology is proposed, and the 3D data registration model is constructed by combining augmented reality technology, build the evaluation index of 3D point cloud data registration, and carry out the initial registration of 3D point cloud based on the evaluation index. The experiment shows that the 3D point cloud data registration algorithm based on augmented reality technology can more effectively improve the accuracy of data registration and avoid the problem of low efficiency of data registration in practice