Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders

Machine Learning (ML) has been applied to enable many life-assisting appli-cations, such as abnormality detection and emdergency request for the soli-tary elderly. However, in most cases machine learning algorithms depend on the layout of the target Internet of Things (IoT) sensor network. Hence, to deploy an application across Heterogeneous Sensor Networks (HSNs), i.e. sensor networks with different sensors type or layouts, it is required to repeat the process of data collection and ML algorithm training. In this paper, we introduce a novel framework leveraging deep learning for graphs to enable using the same activity recognition system across HSNs deployed in differ-ent smart homes. Using our framework, we were able to transfer activity classifiers trained with activity labels on a source HSN to a target HSN, reaching about 75% of the baseline accuracy on the target HSN without us-ing target activity labels. Moreover, our model can quickly adapt to unseen sensor layouts, which makes it highly suitable for the gradual deployment of real-world ML-based applications. In addition, we show that our framework is resilient to suboptimal graph representations of HSNs

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation

In few-shot domain adaptation (FDA), classifiers for the target domain are trained with accessible labeled data in the source domain (SD) and few labeled data in the target domain (TD). However, data usually contain private information in the current era, e.g., data distributed on personal phones. Thus, the private information will be leaked if we directly access data in SD to train a target-domain classifier (required by FDA methods). In this paper, to thoroughly prevent the privacy leakage in SD, we consider a very challenging problem setting, where the classifier for the TD has to be trained using few labeled target data and a well-trained SD classifier, named few-shot hypothesis adaptation (FHA). In FHA, we cannot access data in SD, as a result, the private information in SD will be protected well. To this end, we propose a target orientated hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i.e., an intermediate domain) to help train a target-domain classifier. TOHAN maintains two deep networks simultaneously, where one focuses on learning an intermediate domain and the other takes care of the intermediate-to-target distributional adaptation and the target-risk minimization. Experimental results show that TOHAN outperforms competitive baselines significantly

Transfer Learning based Image Classification of Diseased Tomato Leaves with Optimal Fine-Tuning combined with Heat Map Visualization

Plant disease detection and disease classification at initial stages for sensitive commodities like tomatoes and potatoes is highly mandated as the harvest losses have a direct impact on the price fixation of the vegetables. The most identified limitation in the study of plant pathology is the availability of datasets with visual symptoms that covers all the possible diseases of one crop or plant species. Computer Vision systems and advancements in deep learning-based modeling methodologies gained significant attention in smart farming. It is presumed that the implementation of deep learning algorithms demands a large amount of data to learn complex features automatically and this can pose a challenge for applications with lesser data to achieve generalization. In such cases, Transfer Learning with optimum regularization techniques and fine-tuning mechanisms is the solution to overcome the limitations of smaller datasets. The objective of the work is to develop Tomato Disease Classification System using a transfer learning approach for ten tomato disease classes of the PlantVillage dataset downloaded from the Kaggle platform. Inception V3, a pre-trained transfer learning model is used to classify this multi-class, imbalanced, tomato plant disease based on the leaf symptoms such as dark brown lesions, concentric rings, etc. Geometrical data augmentation is used as a regularization technique to expand the size of the dataset. Significant improvement in the performance metrics is observed when the finetuning is optimum. The training accuracy and validation accuracy of the model before and after fine-tuning are 97.08%, 83.52%, and 98.19%, 95.93% respectively. The average accuracy with augmentation and optimal fine-tuning is 98%. In addition, prediction scores in terms of precision, recall, and F1-score are obtained to visualize the rate of mispredictions across the disease classes. It is observed that the misprediction rate is high across the classes early blight, late blight, and Septoria spot due to similar visual symptoms. Further, activations are used to generate an attention map in the form of Heat Maps which are included as a post-processing step before the classification of the output. Plant Leaf Disease Classification- A web application is deployed using Streamlit Python library and Ngrok services

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly