A Broad Evaluation of the Tor English Content Ecosystem

Tor is among most well-known dark net in the world. It has noble uses, including as a platform for free speech and information dissemination under the guise of true anonymity, but may be culturally better known as a conduit for criminal activity and as a platform to market illicit goods and data. Past studies on the content of Tor support this notion, but were carried out by targeting popular domains likely to contain illicit content. A survey of past studies may thus not yield a complete evaluation of the content and use of Tor. This work addresses this gap by presenting a broad evaluation of the content of the English Tor ecosystem. We perform a comprehensive crawl of the Tor dark web and, through topic and network analysis, characterize the types of information and services hosted across a broad swath of Tor domains and their hyperlink relational structure. We recover nine domain types defined by the information or service they host and, among other findings, unveil how some types of domains intentionally silo themselves from the rest of Tor. We also present measurements that (regrettably) suggest how marketplaces of illegal drugs and services do emerge as the dominant type of Tor domain. Our study is the product of crawling over 1 million pages from 20,000 Tor seed addresses, yielding a collection of over 150,000 Tor pages. We make a dataset of the intend to make the domain structure publicly available as a dataset at https://github.com/wsu-wacs/TorEnglishContent.Comment: 11 page

Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

A Knowledge-Based Topic Modeling Approach for Automatic Topic Labeling

Probabilistic topic models, which aim to discover latent topics in text corpora define each document as a multinomial distributions over topics and each topic as a multinomial distributions over words. Although, humans can infer a proper label for each topic by looking at top representative words of the topic but, it is not applicable for machines. Automatic Topic Labeling techniques try to address the problem. The ultimate goal of topic labeling techniques are to assign interpretable labels for the learned topics. In this paper, we are taking concepts of ontology into consideration instead of words alone to improve the quality of generated labels for each topic. Our work is different in comparison with the previous efforts in this area, where topics are usually represented with a batch of selected words from topics. We have highlighted some aspects of our approach including: 1) we have incorporated ontology concepts with statistical topic modeling in a unified framework, where each topic is a multinomial probability distribution over the concepts and each concept is represented as a distribution over words; and 2) a topic labeling model according to the meaning of the concepts of the ontology included in the learned topics. The best topic labels are selected with respect to the semantic similarity of the concepts and their ontological categorizations. We demonstrate the effectiveness of considering ontological concepts as richer aspects between topics and words by comprehensive experiments on two different data sets. In another word, representing topics via ontological concepts shows an effective way for generating descriptive and representative labels for the discovered topics

The prevalence of depression, anxiety, and stress in patients with breast cancer in Southeast Iran in 2019: a cross-sectional study

Introduction. Today, breast cancer patients suffer from various psychological symptoms that impose irreversible effects on their quality of life. The aim of the present study was to determine the prevalence of depression, anxiety, and stress in patients with breast cancer. Material and methods. This descriptive study was performed on 190 women with breast cancer from January 1, 2019 to July 30, 2019. Data collection was carried out using a convenience sampling method. The Standard Depression, Anxiety, and Stress Scale (DASS-21) was used to assess depression, anxiety, and stress. Results. The mean age of the patients was 46.3 years. Results showed the prevalence of depression, anxiety, and stress to be 28.4%, 43.2%, and 14.7%, respectively. Conclusion. The results indicate that it is vital to measure the level of depression and anxiety in women withbreast cancer, which are two common mental disorders in breast cancer

Assessment of the association between sociodemographic characteristics and response to vitamin D supplementation using artificial neural network

Introduction: The serum 25(OH)D response to vitamin D supplementation, differs  between individuals. The goal of this study was the evaluation of the relationship  between socioeconomic and demographic factors with the hugeness of response to  vitamin supplementation, defined by statistical analysis artificial neural network  (ANNs).  Methods: The prospective interventional study was conducted on 529 participants  aged 19-12 years old. All participants were administrated to receive nine vitamin  D capsules (50000IU vitamin D) over nine weeks. The response variables were the  following: the differences between the concentrations of vitamin D before and after  intervention. Results: Among various sociodemographic factors which affect the increase in  serum vitamin D amounts in response to supplementations, baseline serum vitamin  D (%28.1), BMI (%13.8), physical activity (%12.1), age (%7.6), mother›s education  (%6.4), and father›s occupation (%5.8) be important variables.  Conclusion: This interventional study provides specific sociodemographicrecommendations to achieve 25(OH)D targets in cases with severe vitamin D  deficiency, perhaps indicating that a higher dose is require to obtain optimal Vit D  levels in some individuals

An Introduction to A Robust Framework for Vulnerability Prediction of Infrastructures in Response to Hurricanes

This conference proceeding was published in Proceedings of the Georgia Department of Transportation and Georgia Transportation Institute Research Expo