Prompt-driven Latent Domain Generalization for Medical Image Classification

Deep learning models for medical image analysis easily suffer from distribution shifts caused by dataset artifacts bias, camera variations, differences in the imaging station, etc., leading to unreliable diagnoses in real-world clinical settings. Domain generalization (DG) methods, which aim to train models on multiple domains to perform well on unseen domains, offer a promising direction to solve the problem. However, existing DG methods assume domain labels of each image are available and accurate, which is typically feasible for only a limited number of medical datasets. To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG). PLDG consists of unsupervised domain discovery and prompt learning. This framework first discovers pseudo domain labels by clustering the bias-associated style features, then leverages collaborative domain prompts to guide a Vision Transformer to learn knowledge from discovered diverse domains. To facilitate cross-domain knowledge learning between different prompts, we introduce a domain prompt generator that enables knowledge sharing between domain prompts and a shared prompt. A domain mixup strategy is additionally employed for more flexible decision margins and mitigates the risk of incorrect domain assignments. Extensive experiments on three medical image classification tasks and one debiasing task demonstrate that our method can achieve comparable or even superior performance than conventional DG algorithms without relying on domain labels. Our code will be publicly available upon the paper is accepted.Comment: 10 page

Comparative research: Team learning in higher education

Team learning is the process of aligning and developing the capacity of a team to create the results its members truly desire‟ (Senge, 1990, p 236). This emphasizes the significance of team learning as the fundamental learning units. Despite its importance, team learning among employees in higher education, especially among academics remains poorly understood. This research aims at shedding a light in the area which has recently been urged by the increasingly demanding requirements of interdisciplinary research and teaching in higher education around the world. Through a thorough literature review, a model of team learning has been built with a set of antecedents, two moderators, and the outcome of mental models. Hypotheses were formed, including team commitment, goal setting, development and training, organizational culture, and leadership are positively associated with team learning (antecedents), team learning is positively associated with knowledge sharing (outcome), and better communication systems, and learning environment provide better outcome of team learning (moderators). Thus, the study tested both mediating and Kaleidoscope Postgraduate Conference, Cambridge 2009 http://www.educatejournal.org/ 92 moderating relationships. The data were collected in a form of self-report questionnaires. The model was tested with the data collected from employees of two universities, one in the UK and the other in Vietnam. The findings revealed interesting information on the differences between two universities/two cultures, which is often the benefits of comparative research. The case in VN had more positive results than the case in the UK. There are not many differences between academic and non-academic employees, or between employees who work in science and non-science areas. The research could not avoid some limitations due to self-report questionnaires, though some actions were conducted to reduce research bias. In addition, it is really difficult to measure team performance in higher education, which should have been another outcome of team learning

RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments

Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership. Effective resource sharing, however, remains an open challenge due to the adverse effects that resource contention can have on high-priority, user-facing workloads with strict Quality of Service (QoS) requirements. Although recent approaches have demonstrated promising results, those works remain largely impractical in public cloud environments since workloads are not known in advance and may only run for a brief period, thus prohibiting offline learning and significantly hindering online learning. In this paper, we propose RAPID, a novel framework for fast, fully-online resource allocation policy learning in highly dynamic operating environments. RAPID leverages lightweight QoS predictions, enabled by domain-knowledge-inspired techniques for sample efficiency and bias reduction, to decouple control from conventional feedback sources and guide policy learning at a rate orders of magnitude faster than prior work. Evaluation on a real-world server platform with representative cloud workloads confirms that RAPID can learn stable resource allocation policies in minutes, as compared with hours in prior state-of-the-art, while improving QoS by 9.0x and increasing best-effort workload performance by 19-43%

E-Learning Technology in Distance Education: Identifying Learner Preferences of Knowledge Sharing Tools and Educational Strategy

Electronic learning (e-learning) is a term used to describe any form of education where knowledge sharing is primarily by means of electronic media and communication based technologies. This is one of the methods adopted by distance learning institutions. In Nigeria, many universities are now offering programs by distance learning where the learners in geographically dispersed locations and study at their own pace. The adoption of this form of education by higher institutions in Nigeria is on the increase. There are many tools, media and channels of communication available to these institutions of higher learning. The basic media include e-books, audio, video, computers, and mobile phones. Studies concluded that cultural background and learning styles may influence the effectiveness of the learning tools which could in turn lead to distortion of student learning. It is important that the most suitable of these channels as perceived by the learners themselves be understood. This would enable the institutions to understand the learning styles and expectations of the learners so as to provide the necessary support by focusing on the ones that encourage learning as perceived by the learner. The objective of this study is to understand the learner preferences of e-learning tools used by distance learning institutions in Nigeria. The study used as case study an open and distance learning university that employs e-learning technology as its major means of disseminating information. The study applied questionnaire to collect data from 245 students from two study centers of the National Open University of Nigeria (NOUN). The study found a total of two hundred survey responses to be usable. The study analysis provided results of questions in three categories: Learner-instructor-interaction preferences; Learner-learner-interaction preferences; and Learner ability to learn independently. Results of the study indicated that the most preferred tool for knowledge sharing was significantly different for males and  females ( at , which indicates that for the different sexes different approaches or education  strategies  for distance learning using technology might be better suited based on preferential bias. Results of the study may be useful as decision tools for evaluating and understanding the various teaching/learning tools and communication strategies so as to focus on the ones that encourage learning in our environment. The study recommended that institutions should attempt to use the knowledge transfer method perceived by the learner to be more beneficial to them as they establish the learning method that best suit them. Keywords: learning media, electronic learning, e-learning, distance learning, ICT, educational strategies, knowledge sharin

Collaborative Group Learning

Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically struggle with drastically aggravated student homogenization when the number of students rises. In this paper, we propose Collaborative Group Learning, an efficient framework that aims to diversify the feature representation and conduct an effective regularization. Intuitively, similar to the human group study mechanism, we induce students to learn and exchange different parts of course knowledge as collaborative groups. First, each student is established by randomly routing on a modular neural network, which facilitates flexible knowledge communication between students due to random levels of representation sharing and branching. Second, to resist the student homogenization, students first compose diverse feature sets by exploiting the inductive bias from sub-sets of training data, and then aggregate and distill different complementary knowledge by imitating a random sub-group of students at each time step. Overall, the above mechanisms are beneficial for maximizing the student population to further improve the model generalization without sacrificing computational efficiency. Empirical evaluations on both image and text tasks indicate that our method significantly outperforms various state-of-the-art collaborative approaches whilst enhancing computational efficiency.Comment: Accepted by AAAI 2021; Camera ready versio

Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science

This chapter presents some of the fundamental assumptions and principles that could form the philosophical foundation of GeoAI and spatial data science. Instead of reviewing the well-established characteristics of spatial data (analysis), including interaction, neighborhoods, and autocorrelation, the chapter highlights themes such as sustainability, bias in training data, diversity in schema knowledge, and the (potential lack of) neutrality of GeoAI systems from a unifying ethical perspective. Reflecting on our profession's ethical implications will assist us in conducting potentially disruptive research more responsibly, identifying pitfalls in designing, training, and deploying GeoAI-based systems, and developing a shared understanding of the benefits but also potential dangers of artificial intelligence and machine learning research across academic fields, all while sharing our unique (geo)spatial perspective with others.Comment: Final Draf

Investigating the impact of networking capability on firm innovation performance:using the resource-action-performance framework

The author's final peer reviewed version can be found by following the URI link. The Publisher's final version can be found by following the DOI link.Purpose The experience of successful firms has proven that one of the most important ways to promote co-learning and create successful networked innovations is the proper application of inter-organizational knowledge mechanisms. This study aims to use a resource-action-performance framework to open the black box on the relationship between networking capability and innovation performance. The research population embraces companies in the Iranian automotive industry. Design/methodology/approach Due to the latent nature of the variables studied, the required data are collected through a web-based cross-sectional survey. First, the content validity of the measurement tool is evaluated by experts. Then, a pre-test is conducted to assess the reliability of the measurement tool. All data are gathered by the Iranian Vehicle Manufacturers Association (IVMA) and Iranian Auto Parts Manufacturers Association (IAPMA) samples. The power analysis method and G*Power software are used to determine the sample size. Moreover, SmartPLS 3 and IBM SPSS 25 software are used for data analysis of the conceptual model and relating hypotheses. Findings The results of this study indicated that the relationships between networking capability, inter-organizational knowledge mechanisms and inter-organizational learning result in a self-reinforcing loop, with a marked impact on firm innovation performance. Originality/value Since there is little understanding of the interdependencies of networking capability, inter-organizational knowledge mechanisms, co-learning and their effect on firm innovation performance, most previous research studies have focused on only one or two of the above-mentioned variables. Thus, their cumulative effect has not examined yet. Looking at inter-organizational relationships from a network perspective and knowledge-based view (KBV), and to consider the simultaneous effect of knowledge mechanisms and learning as intermediary actions alongside, to consider the performance effect of the capability-building process, are the main advantages of this research