Human Gait Database for Normal Walk Collected by Smart Phone Accelerometer

The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two endpoints during two different sessions and record their gait data using two smartphones, one was attached to the right thigh and another one on the left side of the waist. This data is collected with the intention to be utilized by a deep learning-based method which requires enough time points. The metadata including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available

Pan-cancer classifications of tumor histological images using deep learning

Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we show that key aspects of cancer image analysis can be performed by deep convolutional neural networks (CNNs) across a wide spectrum of cancer types. In particular, we implement CNN architectures based on Google Inception v3 transfer learning to analyze 27815 H&E slides from 23 cohorts in The Cancer Genome Atlas in studies of tumor/normal status, cancer subtype, and mutation status. For 19 solid cancer types we are able to classify tumor/normal status of whole slide images with extremely high AUCs (0.995±0.008). We are also able to classify cancer subtypes within 10 tissue types with AUC values well above random expectations (micro-average 0.87±0.1). We then perform a cross-classification analysis of tumor/normal status across tumor types. We find that classifiers trained on one type are often effective in distinguishing tumor from normal in other cancer types, with the relationships among classifiers matching known cancer tissue relationships. For the more challenging problem of mutational status, we are able to classify TP53 mutations in three cancer types with AUCs from 0.65-0.80 using a fully-trained CNN, and with similar cross-classification accuracy across tissues. These studies demonstrate the power of CNNs for not only classifying histopathological images in diverse cancer types, but also for revealing shared biology between tumors. We have made software available at: https://github.com/javadnoorb/HistCNNFirst author draf

Optimizing title and Meta tags based on distribution of keywords; Lexical and semantic approaches

Problem statement: To increase traffic on websites, Search Engine Optimization (SEO) has provided many costly and time-consuming options. One problem is the inadequate distribution of keywords especially those keywords that users use the title tag and Meta tags. Approach: This study described work on an initial model for handling some of the SEO factors to increase the distribution of keywords. Our purposed model provide users with the words and their values based on the key weights with initiated formula to provide a new title, keywords, or description in order to increase the relativity between content and HTML Meta tags and title tag. Results: The proposed model had been showed evidence of gaining the greater utilization of the distribution of keywords and prevents recognition of search engine spam. Conclusion: The result shows the significant enhancement of the proposed model on Title Weight by 51.69% of original Title Weight defined by user

A Comparative Study of Abu Yaqube Sejestani and Roman Jakobson’s Communication model and Theory

Every human being needs to send, receive and comprehend messages in a linguistic communication. In other words, Human beings code their massages and decode others’ messages. This communication can occur in a context of six elements, namely, addresser, content, contact, code, message and addressee. Abu Ya'qub Sejestani was an iranian dialectical theologian in the fourth century. Due to his research subject, namely, Ismaili dialectical theology, he studied precisely the mind and language system and the conditions of success or failure in a linguistic communication as an introduction to dialectical discussions. But, unfortunately, his linguistic theories were neglected due to researchers’ dialectical approach to his book Kashf-ul-Mahjoob. This study is going to plan an efficient communication model based on mind and language system and its structure and elements in Sejestani’s viewpoint and compare them with Roman Jakobson’s theory and model of communication to show their common and different aspects and prove that although the discussions about the theory and model of communication became known by  western linguists and, most importantly, by Roman Jacobson, it can be traced in all details and with equivalent terms in Abu Ya'qub Sejestani’s communication theory, which has some advantages over Jakobson’s theory and model

The Effect of Mechanical and Geometric Parameters on the Shear and Axial Failures of Columns in Reinforced Concrete Frames

Experimental research activities and post-earthquake considerations have demonstrated that reinforcedconcrete columns with light or widely spaced transverse reinforcement are vulnerable to shear failure duringearthquakes. According to this point by using failure limit curve, we can assess the effective parameters in shearand axial failure of reinforced concrete columns in framed buildings. In the current study by flexural, shear andaxial springs which are used in series, shear and axial failures and important effective parameters have beenassessed, Besides 5,10 and 15 story models with different amounts of initial axial load ratio have been analyzedby nonlinear push-over analysis. The results of analytical models contain behavior of buildings based on differentinitial axial load ratio and different spacing of transverse reinforcement are compare