Classification with class noises through probabilistic sampling

© 2017 Accurately labeling training data plays a critical role in various supervised learning tasks. Now a wide range of algorithms have been developed to identify and remove mislabeled data as labeling in practical applications might be erroneous due to various reasons. In essence, these algorithms adopt the strategy of one-zero sampling (OSAM), wherein a sample will be selected and retained only if it is recognized as clean. There are two types of errors in OSAM: identifying a clean sample as mislabeled and discarding it, or identifying a mislabeled sample as clean and retaining it. These errors could lead to poor classification performance. To improve classification accuracy, this paper proposes a novel probabilistic sampling (PSAM) scheme. In PSAM, a cleaner sample has more chance to be selected. The degree of cleanliness is measured by the confidence on the label. To accurately estimate the confidence value, a probabilistic multiple voting idea is proposed which is able to assign a high confidence value to a clean sample and a low confidence value to a mislabeled sample. Finally, we demonstrate that PSAM could effectively improve the classification accuracy over existing OSAM methods

Cost-sensitive elimination of mislabeled training data

© 2017 Elsevier Inc. Accurately labeling training data plays a critical role in various supervised learning tasks. Since labeling in practical applications might be erroneous due to various reasons, a wide range of algorithms have been developed to eliminate mislabeled data. These algorithms may make the following two types of errors: identifying a noise-free data as mislabeled, or identifying a mislabeled data as noise free. The effects of these errors may generate different costs, depending on the training datasets and applications. However, the cost variations are usually ignored thus existing works are not optimal regarding costs. In this work, the novel problem of cost-sensitive mislabeled data filtering is studied. By wrapping a cost-minimizing procedure, we propose the prototype cost-sensitive ensemble learning based mislabeled data filtering algorithm, named CSENF. Based on CSENF, we further propose two novel algorithms: the cost-sensitive repeated majority filtering algorithm CSRMF and cost-sensitive repeated consensus filtering algorithm CSRCF. Compared to CSENF, these two algorithms could estimate the mislabeling probability of each training data more confidently. Therefore, they produce less cost compared to CSENF and cost-blind mislabeling filters. Empirical and theoretical evaluations on a set of benchmark datasets illustrate the superior performance of the proposed methods

A study of perturbations in linear and circular polarized antennas in close proximity to the human body and a dielectric liquid filled phantom at 1.8 GHz

In the design and synthesis of wearable antennas isolation distance from the body is a critical parameter. This paper deals with the comparison of perturbations caused to the matching of simple linear and circular polarized patch antennas due to the close proximity of a human torso and rectangular box phantom filled with muscle simulating liquid at 1.8GHz. The isolated variable is return loss (S11). Results show that both linear and circularly polarized antennas produce an optimal return loss closer to the surface of a typical phantom than the back of a human volunteer

Assessment of genes controlling Area Under Disease Progress Curve (AUDPC) for stripe rust (P. striiformis f. sp. Tritici) in two wheat (Triticum aestivum L.) crosses

Genetic effects on controlling stripe rust resistance were determined in two wheat crosses, Bakhtawar-92 x Frontana (cross 1) and Inqilab-91 x Fakhre Sarhad (cross 2) using Area under Disease Progress Curve (AUDPC) as a measure of stripe rust resistance.Генетические эффекты контроля устойчивости к желтой ржавчине злаков были определены в двух скрещиваниях пшеницы Bakhtawar-92 x Frontana (скрещивание 1) и Inquilab-91 x Fakhre-Sarhad (скрещивание 2) с использованием Area Under Disease Progress Curve (AUDPC) для измерения устойчивости.Генетичні ефекти контролю стійкості до жовтої іржі злаків були визначені в двох схрещуваннях пшениці Bakhtawar-92 x Frontana (схрещування 1) и Inquilab-91 x Fakhre-Sarhad (схрещування 2) з використанням Area Under Disease Progress Curve (AUDPC) для вимірювання стійкості

Isolation and detection of circulating tumour cells from metastatic melanoma patients using a slanted spiral microfluidic device.

Circulating Tumour Cells (CTCs) are promising cancer biomarkers. Several methods have been developed to isolate CTCs from blood samples. However, the isolation of melanoma CTCs is very challenging as a result of their extraordinary heterogeneity, which has hindered their biological and clinical study. Thus, methods that isolate CTCs based on their physical properties, rather than surface marker expression, such as microfluidic devices, are greatly needed in melanoma. Here, we assessed the ability of the slanted spiral microfluidic device to isolate melanoma CTCs via label-free enrichment. We demonstrated that this device yields recovery rates of spiked melanoma cells of over 80% and 55%, after one or two rounds of enrichment, respectively. Concurrently, a two to three log reduction of white blood cells was achieved with one or two rounds of enrichment, respectively. We characterised the isolated CTCs using multimarker flow cytometry, immunocytochemistry and gene expression. The results demonstrated that CTCs from metastatic melanoma patients were highly heterogeneous and commonly expressed stem-like markers such as PAX3 and ABCB5. The implementation of the slanted microfluidic device for melanoma CTC isolation enables further understanding of the biology of melanoma metastasis for biomarker development and to inform future treatment approaches

Security requirement management for cloud-assisted and internet of things⇔enabled smart city

The world is rapidly changing with the advance of information technology. The expansion of the Internet of Things (IoT) is a huge step in the development of the smart city. The IoT consists of connected devices that transfer information. The IoT architecture permits on-demand services to a public pool of resources. Cloud computing plays a vital role in developing IoT-enabled smart applications. The integration of cloud computing enhances the offering of distributed resources in the smart city. Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability, security, performance, confidentiality, and privacy. The key reason for cloud- and IoT-enabled smart city application failure is improper security practices at the early stages of development. This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications. Its three-layered architecture includes privacy preserved stakeholder analysis (PPSA), security requirement modeling and validation (SRMV), and secure cloud-assistance (SCA). A case study highlights the applicability and effectiveness of the proposed framework. A hybrid survey enables the identification and evaluation of significant challenges