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The emergence of Consumer-to-Consumer (C2C) platforms has allowed consumers to buy and sell goods directly, but it has also created problems, such as commodity fraud and fake reviews. Trust Management Algorithms (TMAs) are expected to be a countermeasure to detect fraudulent users. However, it is unknown whether TMAs are as effective as reported as they are designed for Peer-to-Peer (P2P) communications between devices on a network. Here we examine the applicability of `EigenTrust', a representative TMA, for the use case of C2C services using an agent-based model. First, we defined the transaction process in C2C services, assumed six types of fraudulent transactions, and then analysed the dynamics of EigenTrust in C2C systems through simulations. We found that EigenTrust could correctly estimate low trust scores for two types of simple frauds. Furthermore, we found the oscillation of trust scores for two types of advanced frauds, which previous research did not address. This suggests that by detecting such oscillations, EigenTrust may be able to detect some (but not all) advanced frauds. Our study helps increase the trustworthiness of transactions in C2C services and provides insights into further technological development for consumer services.

The conveniences experienced by society have tremendously improved with the development of the Internet of Things (IoT). Among the affordances stemming from this innovation is an IoT concept called the SmartHome, which is already spreading even in general households. Despite this proliferation, however, ordinary users experience difficulty in performing the complex control and automation of IoT devices, thereby impeding their full exploitation of IoT benefits. These problems highlight the need for a system that enables general users to easily manipulate IoT devices. Correspondingly, this study constructed a visual programming system that facilitates IoT device operation. The system, which was developed on the basis of data obtained from various sensors in a SmartHome, employs mixed reality(MR) in enhancing the visualization of various data, eases the understanding of the positional relationship among devices, and smoothens the checking of execution results. We conducted an evaluation experiment wherein eight users were asked to test the proposed system, and we verified its usefulness on the basis of the time elapsed until the participants completed the programming of diverse IoT devices and a questionnaire intended to derive their subjective assessments. The result indicates that the proposed system makes it easy to understand the correspondence between the real world device and the node in the MR environment, and the connection between the sensors and the home appliances. On the other hand, it is negatively evaluated for operability., ICAT-EGVE 2019 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments, Design and Programming, 105, 112, Ryohei Suzuki, Katsutoshi Masai, and Maki Sugimoto, CCS Concepts: Human-centered computing --> Ubiquitous and mobile computing; Mixed / augmented reality

Comparison of gene expression levels of samples with different RNA mixture ratios (PANC-1 and SVEC4-10) estimated by CASTIN. (a) gene expression levels of human (human content: 25 %, 50 %, 75 %, 100 %) and (b) mouse (mouse content: 100 %, 75 %, 50 %, 25 %) after global normalization in each species. On the bottom of the diagonal: the bivariate scatter plots with the identity line. On the top of the diagonal: the value of the cosine correlation. (PDF 608 kb)

Read count biases in a xenograft sample from PDAC cell line (PK-1). Randomly chosen 100,000 read count residuals from fitted model are plotted against (a, b) distance from poly-A site and (c, d) regional GC content. Residuals with biases (a, c) and without biases (b, d). Randomly chosen 100,000 points are plotted. Straight line in each plot indicates the bias estimated from 200 genes using Poisson linear model (see Methods). (PDF 70 kb)

Summary statistics of RNA-seq for mixture of total RNA from human (PANC-1) and mouse (SVEC4-10) cell lines. (PDF 37 kb)

Distribution of hedgehog-related interactions in PDAC samples. a) Cancer cell SHH to stromal PTCH1. b) Cancer-cell IHH to stromal PTCH1. c) Cancer-cell IHH to stromal PTCH2. (PDF 162 kb)

Screenshot of the CASTIN interactive viewer. Hover a cursor over a circle and it displays the interacting genes represented and their value of signal strength. (PDF 1376 kb)

Distribution of mutually dependent interactions with strong signals in PDAC samples. a) Stromal SEMA3C to cancer-cell PLXNB1. b) Cancer-cell SEMA4D to stromal NRP1. (PDF 136 kb)

Comparison of gene expression levels of samples with different RNA mixture ratios (PANC-1 and SVEC4-10) estimated by CASTIN. (a) gene expression levels of human (human content: 25 %, 50 %, 75 %, 100 %) and (b) mouse (mouse content: 100 %, 75 %, 50 %, 25 %) after global normalization in each species. On the bottom of the diagonal: the bivariate scatter plots with the identity line. On the top of the diagonal: the value of the cosine correlation. (PDF 608 kb)

Distribution of mutually dependent interactions with strong signals in PDAC samples. a) Stromal SEMA3C to cancer-cell PLXNB1. b) Cancer-cell SEMA4D to stromal NRP1. (PDF 136 kb)

FABP5/Fabp5 expression in RNA-Seq (estimated by CASTIN) and Immunohistochemistry. (a) gene expression levels. (b) Immunohistochemical (IHC) and hematoxylin and eosin (H&E) staining of close sections. Sections of PDAC xenograft cancer derived from each cell line were stained for FABP5/Fabp5 (left) or with H&E (right). The slightly different distribution of tumor and stromal cells between H&E and the corresponding IHC sections was due to the physical distance between the two sections. (PDF 10295 kb)

FABP5/Fabp5 expression in RNA-Seq (estimated by CASTIN) and Immunohistochemistry. (a) gene expression levels. (b) Immunohistochemical (IHC) and hematoxylin and eosin (H&E) staining of close sections. Sections of PDAC xenograft cancer derived from each cell line were stained for FABP5/Fabp5 (left) or with H&E (right). The slightly different distribution of tumor and stromal cells between H&E and the corresponding IHC sections was due to the physical distance between the two sections. (PDF 10295 kb)

Summary statistics of RNA-seq for mixture of total RNA from human (PANC-1) and mouse (SVEC4-10) cell lines. (PDF 37 kb)

Ligand-receptor interactions used in the CASTIN system. (PDF 175 kb)

Distribution of hedgehog-related interactions in PDAC samples. a) Cancer cell SHH to stromal PTCH1. b) Cancer-cell IHH to stromal PTCH1. c) Cancer-cell IHH to stromal PTCH2. (PDF 162 kb)

Background: There few biomarkers of asthma which can be easily assessed for clinical use, especially in children. Periostin is an extracellular matrix protein and is upregulated by IL-4 and IL-13 stimulation from airway epithelial cells and other structural cells. Serum periostin is expected as a non-invasive biomarker for asthma diagnosis and management. Purpose: To assess the usefulness of serum periostin levels as a clinical biomarker of pediatric asthma. Method: 29 asthmatic children (BA) and 27 non-asthma control children (Cont) were investigated (6-16 years). Bronchial asthma was diagnosed by ISAAC medical questionnaire, and methacholine challenge test was performed for BA. Comorbid diseases including heart disease, renal disease, skeletal disease and atopic dermatitis were excluded. FeNO, lung function, blood eosinophil counts, serum levels of IgE, periostin were assessed. The diagnostic accuracy of these markers to identify asthma was calculated using receiver operating characteristics area under the curve (ROC AUC). Results: Mean PC20 of methacholine was 1.1 mg/ml. Serum periostin levels in BAwas significantly higher than in Cont (mean ± SD: 138.3 ± 34.9 vs 119.2 ± 35.0 ng/ml,p