AN ANALYSIS OF OKLAHOMA DIRECT MARKETING OUTLETS: CASE STUDY OF PRODUCE FARMERS' MARKETS

The objectives of this study are to examine consumer preferences among various marketing channels including direct marketing in Oklahoma, and to analyze the impact of various demographic variables on purchasing choice. Specifically, this research focuses on the links between demographic factors and shopping preferences. Data from consumers' survey in 21 farmers' markets in Oklahoma will be used to analyze consumer preferences using an ordered logistic regression analysis method. Farmers' market producers and market managers were also surveyed. The results of this study identify consumer characteristics that influence produce demand and consequently growers' return at Oklahoma farmers' market.Consumer/Household Economics,

End-to-End Localization and Ranking for Relative Attributes

We propose an end-to-end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly-supervised pairwise image comparisons. Unlike previous methods, our network jointly learns the attribute's features, localization, and ranker. The localization module of our network discovers the most informative image region for the attribute, which is then used by the ranking module to learn a ranking model of the attribute. Our end-to-end framework also significantly speeds up processing and is much faster than previous methods. We show state-of-the-art ranking results on various relative attribute datasets, and our qualitative localization results clearly demonstrate our network's ability to learn meaningful image patches.Comment: Appears in European Conference on Computer Vision (ECCV), 201

BATS: Binary ArchitecTure Search

This paper proposes Binary ArchitecTure Search (BATS), a framework that drastically reduces the accuracy gap between binary neural networks and their real-valued counterparts by means of Neural Architecture Search (NAS). We show that directly applying NAS to the binary domain provides very poor results. To alleviate this, we describe, to our knowledge, for the first time, the 3 key ingredients for successfully applying NAS to the binary domain. Specifically, we (1) introduce and design a novel binary-oriented search space, (2) propose a new mechanism for controlling and stabilising the resulting searched topologies, (3) propose and validate a series of new search strategies for binary networks that lead to faster convergence and lower search times. Experimental results demonstrate the effectiveness of the proposed approach and the necessity of searching in the binary space directly. Moreover, (4) we set a new state-of-the-art for binary neural networks on CIFAR10, CIFAR100 and ImageNet datasets. Code will be made available https://github.com/1adrianb/binary-nasComment: accepted to ECCV 202

Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks

Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks, ranging from voice recognition, object detection, to image classification. However, their increasing complexity might yield poor generalization error that make them hard to be deployed on edge devices. Quantization is an effective approach to compress DNNs in order to meet these constraints. Using a quasiconvex base function in order to construct a binary quantizer helps training binary neural networks (BNNs) and adding noise to the input data or using a concrete regularization function helps to improve generalization error. Here we introduce foothill function, an infinitely differentiable quasiconvex function. This regularizer is flexible enough to deform towards $L_1$ and $L_2$ penalties. Foothill can be used as a binary quantizer, as a regularizer, or as a loss. In particular, we show this regularizer reduces the accuracy gap between BNNs and their full-precision counterpart for image classification on ImageNet.Comment: Accepted in 16th International Conference of Image Analysis and Recognition (ICIAR 2019

(Dynamic) Prompting might be all you need to repair Compressed LLMs

Large language models (LLMs), while transformative for NLP, come with significant computational demands, underlining the need for efficient, training-free compression. Notably, despite the marked improvement in training-free compression for the largest of LLMs, our tests using LLaMA-7B and OPT-6.7b highlight a significant performance drop in several realistic downstream tasks. Investigation into the trade-off between resource-intensive post-compression re-training highlights the prospect of prompt-driven recovery as a lightweight adaption tool. However, existing studies, confined mainly to perplexity evaluations and simple tasks, fail to offer unequivocal confidence in the scalability and generalizability of prompting. We tackle this uncertainty in two key ways. First, we uncover the vulnerability of naive prompts in LLM compression as an over-reliance on a singular prompt per input. In response, we propose inference-time dynamic prompting (IDP), a mechanism that autonomously chooses from a set of curated prompts based on the context of each individual input. Second, we delve into a scientific understanding of why "prompting might be all you need post-LLM compression." Our findings suggest that compression does not irretrievably erase LLM model knowledge but displace it, necessitating a new inference path. IDP effectively redirects this path, enabling the model to tap into its inherent yet displaced knowledge and thereby recover performance. Empirical tests affirm the value of IDP, demonstrating an average performance improvement of 1.24% across nine varied tasks spanning multiple knowledge domains

Resistance of wheat cultivars to bird cherry-oat aphid, Rhopalosiphum padi (Hem.: Aphididae)

The bird cherry-oat aphid, Rhopalosiphum padi (L.), is polyphagous with a nearly worldwide distribution and known as an important pest of wheat and the main vector of barley yellow dwarf virus. In this study, the possibility of antixenosis, antibiosis and tolerance of six common wheat cultivars of Chamran, Darab 2, Shiraz, Ghods, Marvdasht and Niknezhad was investigated at 2-3 leaf growth stage in the Fars province, Iran. The experiments were conducted at 24 ± 5°C, 65 ± 5% R.H. and natural light in a greenhouse, using a randomized complete design. The analysis of variance in regard to the number of adult aphids attracted to each cultivar, was indicative of significant differences among the cultivars (P < 0.01). The highest (21 ± 0.71) and the lowest (11.6 ± 0.51) mean number of adult aphids attracted per plant was observed on Shiraz and Darab 2, respectively. The antibiosis test, based on nymphs per female was significantly different among the cultivars (P < 0.01) whose average values were 62.05, 55.84, 49.89, 47.63, 42.76 and 40.65 nymphs per female on Niknezhad, Shiraz, Ghods, Marvdash, Chamran, and Darab 2, respectively. The tolerance studies based on the damage index, showed that Chamran and Darab 2, with the lowest damage index (1.33), were the most tolerant cultivars while Shiraz and Niknezhad, with the highest damage indexes of 3.00 and 3.67 respectively, were the most susceptible cultivars. The cutivars Niknezhad and Shiraz are found to be susceptible, Ghods and Marvdasht partially resistant, and Chamran and Darab 2 resistant to the bird cherry - oat aphid

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques