High-Level Concepts for Affective Understanding of Images

This paper aims to bridge the affective gap between image content and the emotional response of the viewer it elicits by using High-Level Concepts (HLCs). In contrast to previous work that relied solely on low-level features or used convolutional neural network (CNN) as a black-box, we use HLCs generated by pretrained CNNs in an explicit way to investigate the relations/associations between these HLCs and a (small) set of Ekman's emotional classes. As a proof-of-concept, we first propose a linear admixture model for modeling these relations, and the resulting computational framework allows us to determine the associations between each emotion class and certain HLCs (objects and places). This linear model is further extended to a nonlinear model using support vector regression (SVR) that aims to predict the viewer's emotional response using both low-level image features and HLCs extracted from images. These class-specific regressors are then assembled into a regressor ensemble that provide a flexible and effective predictor for predicting viewer's emotional responses from images. Experimental results have demonstrated that our results are comparable to existing methods, with a clear view of the association between HLCs and emotional classes that is ostensibly missing in most existing work

Collaborative Crop Research Program

For over 30 years, The McKnight Foundation's Collaborative Crop Research Program (CCRP) has explored solutions for sustainable local food systems through agricultural research. The program grew out of the Foundation's Plant Biology Program, which was founded in 1983, and reflects the Foundation's long-time commitment to place-based grantmaking and learning from those working on the ground. In 2014, the Foundation engaged The Philanthropic Initiative (TPI) to develop a historic overview of the CCRP to capture its origins and evolution over the last 30 years. To develop this narrative, TPI interviewed past and current Board members, staff, consultants and grantees who had been involved at various stages in the lifespan of the program, and reviewed existing documents, reports and meeting notes.The report that follows is to serve as part of the "institutional memory" of The McKnight Foundation's Collaborative Crop Research Program. Its heavy reliance on individual recollections may detract from its precision, but such reflections bring to life the program's three decades of commitment, collaboration, and adaptation in an effort to contribute to a world where all have access to nutritious food that is sustainably produced by local people. While not an evaluative document, key moments of influence and impacts are noted along the way

Exploring the Potential of Developmental Work Research and Change Laboratory to Support Sustainability Transformations:A Case Study of Organic Agriculture in Zimbabwe

This paper explores the emergence of transgressive learning in CHAT-informed development work research in a networked organic agriculture case study in Zimbabwe, based on intervention research involving district organic associations tackling interconnected issues of climate change, water, food security and solidarity. The study established that We change laboratories can be used to support transgressive learning through: confronting unproductive local norms; collective reframing of problematic issues; stimulating expansive learning and sustainability transformations in minds, relationships and landscapes across time. The study also confirms the need for fourth generation CHAT to address the complex social-ecological problems of today

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Delphine Red Shirt: George Sword's Warrior Narratives: Compositional Processes in Lakota Oral Tradition

George Sword an Oglala Lakota (1846–1914) learned to write in order to transcribe and preserve his people’s oral narratives. In her book Delphine Red Shirt, also Oglala Lakota and a native speaker, examines the compositional processes of George Sword and shows how his writings reflect recurring themes and story patterns of the Lakota oral tradition. Her book invites further studies in several areas including literature, translation studies and more. My review of her book suggests some ways it could be used as a primary resource book in developing curricula in Indigenous philosoph