Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels. With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201

Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

Reliability and Validity of the Taiwan Chinese Version of the EORTC QLQ-PR25 in Assessing Quality of Life of Prostate Cancer Patients

Background/PurposeThis study examined the psychometric properties and clinical validity of the EORTC QLQ-PR25, a questionnaire for assessing the quality of life of patients with prostate cancer.MethodsThe Taiwan Chinese version of the prostate cancer module (EORTC QLQ-PR25) and the core questionnaires (EORTC QLQ-C30) were administered to 81 patients with prostate cancer after they had been treated with surgery or hormone therapy or both. The QLQ-PR25 module assesses urinary symptoms, bowel symptoms, hormonal treatment-related symptoms, sexual activity and sexual functioning.ResultsThe questionnaires were well accepted by the patients and very few of the items had missing data. Only the urinary symptom scale showed satisfactory internal consistency. Scales were able to differentiate clinical groups of patients with corresponding symptoms, but the differences were smaller than that of major functioning scales in the core questionnaire.ConclusionThe Taiwan Chinese version of the EORTC QLQ-PR25 is acceptable in patients with prostate cancer in Taiwan, able to differentiate corresponding symptoms, but the scale structure needs further improvement

A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables

[[abstract]]Maximum power point tracking (MPPT) is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i) slope (of solar power-versus-solar voltage) and changes of the slope; (ii) slope and variation of the power; (iii) variation of power and variation of voltage; (iv) variation of power and variation of current; (v) sum of conductance and increment of the conductance; and (vi) sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i)–(iv) have two input variables each while algorithms (v) and (vi) use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV) cell properties. The range of the input variable of Algorithm (vi) is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]電子版[[countrycodes]]CH

On the Patent Claim Eligibility Prediction Using Text Mining Techniques

With the widespread of computer software in recent decades, software patent has become controversial for the patent system. Of the many patentability requirements, patentable subject matter serves as a gatekeeping function to prevent a patent from preempting future innovation. Software patents may easily fall into the gray area of abstract ideas, whose allowance may hinder future innovation. However, without a clear definition of abstract ideas, determining the patent claim subject matter eligibility is a challenging task for examiners and applicants. In this research, in order to solve the software patent eligibility issues, we propose an effective model to determine patent claim eligibility by text-mining and machine learning techniques. Drawing upon USPTO issued guidelines, we identify 66 patent cases to design domain knowledge features, including abstractness features and distinguishable word features, as well as other textual features, to develop the claim eligibility prediction model. The experiment results show our proposed model reaches the accuracy of more than 80%, and domain knowledge features play a crucial role in our prediction model