Classifying Illegal Advertisements on the Darknet Using NLP

The Darknet has become a place to conduct various illegal activities like child labor, contract murder, drug selling while staying anonymous. Traditionally, international and government agencies try to control these activities, but most of those actions are manual and time-consuming. Recently, various researchers developed Machine Learning (ML) approaches trying to aid in the process of detecting illegal activities. The above problem can benefit by using different Natural Language Processing (NLP) techniques. More specifically, researchers have used various classical topic modeling techniques like bag of words, N-grams, Term Frequency, Term Frequency Inverse Document Frequency (TF-IDF) to represent features and train machine learning models. Moreover, researchers have used an imbalanced dataset to perform those experiments. In this work, we use some more modern techniques like Doc2Vec, Bidirectional Encoder Representation From Transformers (BERT) that have not been studied yet. The primary problem of this project is to classify illegal advertisements published on the Darknet by exploring the above-mentioned state of the art and comparing them against known approaches that use classical techniques, like TF-IDF. Also, we use various data balancing techniques and perform experiments using that data on classical techniques like TF-IDF

Optimal anti-ferromagnets for light dark matter detection

We propose anti-ferromagnets as optimal targets to hunt for sub-MeV dark matter with spin-dependent interactions. These materials allow for multi-magnon emission even for very small momentum transfers, and are therefore sensitive to dark matter particles as light as the keV. We use an effective theory to compute the event rates in a simple way. Among the materials studied here, we identify nickel oxide (a well-assessed anti-ferromagnet) as an ideal candidate target. Indeed, the propagation speed of its gapless magnons is very close to the typical dark matter velocity, allowing the absorption of all its kinetic energy, even through the emission of just a single magnon

Cultural disgrace among tuberculosis patients in Sagar district of Madhya Pardesh in India

Background: The overall objective of the research study was to gather new empirical evidence and develop further theoretical understanding of the mechanisms of stigma associated with TB and delay in seeking its treatment.Methods: This prospective study was carried out in the outpatient of Pulmonary Medicine at Bundelkhand Government Medical College, Sagar. The sample consisted of 300 tuberculosis patients. Between patients who consider TB a stigmatizing disease and patients who did not consider TB a stigmatizing disease distribution of patient delay was compared.Results: Of the total of 300 patients 79 (26.3%) considered TB a socially stigmatizing disease. Among them 43 (54.4%) were females and 36 (45.6%) males. Among patients in the age group 18-24 years, nine (50%) considered TB a socially stigmatizing disease compared to seven (12.3%) among patients in the age group 65-75 years. The average time interval from the appearance of first symptoms of tuberculosis until the first visit to a health care facility for those who consider TB a stigmatizing disease was 6.41 weeks and for those who did not consider it a stigmatizing disease the average time interval was 4.99 weeks.Conclusions: Most TB patients failed to recognize their symptoms as due to TB, because of the stigma attached to the disease in society. The way people treat those with TB, especially close contacts is also a source of worry to the patients. This may lead to delay in reporting to the hospital and consequently increase mortality from the disease. It may also make it difficult for the patients to comply with the long duration of TB treatment. Study results revealed high stigma-generating attitudes towards tuberculosis.

An Unusual Case of a Gastric Xanthoma: A Case Report.

Gastric xanthomas are rare tumor-like lesions, most commonly occurring in the antral region. We set out to describe a patient with a history of Barrett\u27s esophagus status post two radiofrequency ablations (RFAs) and an endoscopic mucosal resection (EMR) who developed a gastric xanthoma just below the Z-line with recurrent esophageal metaplasia. Histopathological confirmation of xanthomas are needed to rule out malignancy. While gastric xanthomas themselves are benign conditions, regular follow-up is recommended if there is a high index of suspicion of malignancy or alarming symptoms develop