A Complementary Measure of MIS Program Outcomes: Useful Insights from a Student Perspective

Assessing student learning is a critical element in today’s higher education environment. Learning assurance programs seek to assess and improve the quality of student learning, and may employ both direct and indirect measures. In this paper, we describe a practical learning assurance assessment measure developed and used as a part of a broader program to evaluate and monitor the learning of students in our Management Information Systems major. This measure enables us to evaluate our students’ learning as reflected by their confidence, persistence, and willingness to undertake MIS-related tasks. We believe this is an important indicator of learning. This paper describes our development of this measure, use of the measure as an element of our learning assurance program for our MIS major, and insights gained from this assessment approach

From Short-term Hotspot Measurements to Long-term Module Reliability

AbstractIn order to reach high module reliability, all solar cells with a potentially critical hotspot have to be neglected during cell sorting. This is essential to avoid delamination in case of partial shading of the module. Due to throughput considerations, the finished solar cell has to be classified within some milliseconds. In consequence the short-term hotspot heating measurement has to be correlated to absolute hotspot temperatures for various module conditions in the field. Previously it has already been shown that a definite mapping of these quantities is not possible, requiring further investigations in order to quantify the risk for possible module damage.In this contribution, the probability distribution for absolute hotspot temperatures in the module will be calculated from short-term hotspot measurement data, considering temperature-dependent reverse biases. Together with experimental data for module delamination temperatures, the probability of module failure can be calculated in a direct way

Momentum-based Weight Interpolation of Strong Zero-Shot Models for Continual Learning

Large pre-trained, zero-shot capable models have shown considerable success both for standard transfer and adaptation tasks, with particular robustness towards distribution shifts. In addition, subsequent fine-tuning can considerably improve performance on a selected downstream task. However, through naive fine-tuning, these zero-shot models lose their generalizability and robustness towards distribution shifts. This is a particular problem for tasks such as Continual Learning (CL), where continuous adaptation has to be performed as new task distributions are introduced sequentially. In this work, we showcase that where fine-tuning falls short to adapt such zero-shot capable models, simple momentum-based weight interpolation can provide consistent improvements for CL tasks in both memory-free and memory-based settings. In particular, we find improvements of over $+4\%$ on standard CL benchmarks, while reducing the error to the upper limit of jointly training on all tasks at once in parts by more than half, allowing the continual learner to inch closer to the joint training limits.Comment: First Workshop on Interpolation Regularizers and Beyond, NeurIPS 2022 (Spotlight) and Workshop on Distribution Shifts, NeurIPS 202

Teaching Programming Via The Web: A Time-Tested Methodology

Advances in information and communication technologies give us the ability to reach out beyond the time and place limitations of the traditional classroom. However, effective online teaching is more than just transferring traditional courses to the World Wide Web (WWW). We describe how we have used “off the shelf” software and the infrastructure that is available via the WWW to develop and deliver a complete learning experience in programming business applications using a popular programming language. The course is unique in its coordinated use of traditional and nontraditional materials to introduce and explain difficult programming constructs. Student performance, job placement, and feedback have confirmed the convenience and effectiveness of this method

Vision-by-Language for Training-Free Compositional Image Retrieval

Given an image and a target modification (e.g an image of the Eiffel tower and the text "without people and at night-time"), Compositional Image Retrieval (CIR) aims to retrieve the relevant target image in a database. While supervised approaches rely on annotating triplets that is costly (i.e. query image, textual modification, and target image), recent research sidesteps this need by using large-scale vision-language models (VLMs), performing Zero-Shot CIR (ZS-CIR). However, state-of-the-art approaches in ZS-CIR still require training task-specific, customized models over large amounts of image-text pairs. In this work, we propose to tackle CIR in a training-free manner via our Compositional Image Retrieval through Vision-by-Language (CIReVL), a simple, yet human-understandable and scalable pipeline that effectively recombines large-scale VLMs with large language models (LLMs). By captioning the reference image using a pre-trained generative VLM and asking a LLM to recompose the caption based on the textual target modification for subsequent retrieval via e.g. CLIP, we achieve modular language reasoning. In four ZS-CIR benchmarks, we find competitive, in-part state-of-the-art performance - improving over supervised methods. Moreover, the modularity of CIReVL offers simple scalability without re-training, allowing us to both investigate scaling laws and bottlenecks for ZS-CIR while easily scaling up to in parts more than double of previously reported results. Finally, we show that CIReVL makes CIR human-understandable by composing image and text in a modular fashion in the language domain, thereby making it intervenable, allowing to post-hoc re-align failure cases. Code will be released upon acceptance