Resiliency in Deep Convolutional Neural Networks

The enormous success and popularity of deep convolutional neural networks for object detection has prompted their deployment in various real-world applications. However, their performance in the presence of hardware faults or damage that could occur in the field has not been studied. This thesis explores the resiliency of six popular network architectures for image classification, AlexNet, VGG16, ResNet, GoogleNet, SqueezeNet and YOLO9000, when subjected to various degrees of failures. We introduce failures in a deep network by dropping a percentage of weights at each layer. We then assess the effects of these failures on classification performance. We find the fitness of the weights and then dropped from least fit to most fit weights. Finally, we determine the ability of the network to self-heal and recover its performance by retraining its healthy portions after partial damage. We try different methods to re-train the healthy portion by varying the optimizer. We also try to find the time and resources required for re-training. We also reduce the number of parameters in GoogleNet, VGG16 to the size of SqueezeNet and re-trained with varying percentage of dataset. This can be used as a network pruning method

Food sovereignty and consumer sovereignty: two antagonistic goals?

The concept of food sovereignty is becoming an element of everyday parlance in development politics and food justice advocacy. Yet to successfully achieve food sovereignty, the demands within this movement have to be compatible with the way people are pursuing consumer sovereignty, and vice versa. The aim of this article is to examine the different sets of demands that the two ideals of sovereignty bring about, analyze in how far these different demands can stand in constructive relations with each other and explain why consumers have to adjust their food choices to seasonal production variability to promote food sovereignty and so secure future autonomy

Context-aware Collaborative Neuro-Symbolic Inference in Internet of Battlefield Things

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features integrated neuro-symbolic inference, where symbolic context is used by deep learning, and deep learning models provide atomic concepts for symbolic reasoning. The incorporation of high-level symbolic reasoning improves data efficiency during training and makes inference more robust, interpretable, and resource-efficient. In this paper, we identify the key challenges in developing context-aware collaborative neuro-symbolic inference in IoBTs and review some recent progress in addressing these gaps