Detection of Hidden Communities in Twitter Discussions of Varying Volumes

The community-based structure of communication on social networking sites has long been a focus of scholarly attention. However, the problem of discovery and description of hidden communities, including defining the proper level of user aggregation, remains an important problem not yet resolved. Studies of online communities have clear social implications, as they allow for assessment of preference-based user grouping and the detection of socially hazardous groups. The aim of this study is to comparatively assess the algorithms that effectively analyze large user networks and extract hidden user communities from them. The results we have obtained show the most suitable algorithms for Twitter datasets of different volumes (dozen thousands, hundred thousands, and millions of tweets). We show that the Infomap and Leiden algorithms provide for the best results overall, and we advise testing a combination of these algorithms for detecting discursive communities based on user traits or views. We also show that the generalized K-means algorithm does not apply to big datasets, while a range of other algorithms tend to prioritize the detection of just one big community instead of many that would mirror the reality better. For isolating overlapping communities, the GANXiS algorithm should be used, while OSLOM is not advised.

NMDB@Home

An overview on the presentations at the first virtual symposium on cosmic ray studies with neutron detectors is given. The meeting was held online in July 2020. Neutron detectors on ground are shown to provide significant contributions to research on interactions of galactic cosmic rays with magnetic fields in the Heliosphere and on the acceleration of energetic particles, as well as to a growing range of applications, including geophysics and space weather. The advent of easily accessible databases makes original data easily available to a large user community. The present overview outlines and introduces the more detailed articles contained in the proceedings.

QED: A Framework and Dataset for Explanations in Question Answering

A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility, and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks—post- hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.

Making Meaningful User Segments from Datasets Using Product Dissemination and Product Impact

Online companies face large user populations, making segmentation a daunting exercise. Demonstrating an approach that facilitates user segmentation, this research leverages product dissemination and product impact metrics with normalized Shannon entropy. Using 4,653 products from an international news and media organization with 134,364,449 user-product engagements, we isolate the key products with the widest product dissemination and the least product impact using entropy-based measures, effectively capturing the engagement levels. We demonstrate that a small percentage (0.33% in our dataset) of products are so widely disseminated that they are non-discriminatory, and a large percentage of products (17.02%) are discriminatory but have so little dissemination that their impact is negligible. Our approach reduces the product dataset by 17.35% and the number of user segments by 8.18%. Implications are that organizations can isolate impactful products useful for user segmentation to enhance the user focus.

The concept of organizing educational literary tourism by means of information technologies as a region development tool

The article discusses the development of the cultural potential of the region through the development of tourism. Educational tourism was chosen as one of the potentially attractive types of tourism, as well as its private direction - literary tourism, which has specificity in terms of methodological, organizational and communicational components. In the presented concept of organizing literary tourism, the development of an information system is described that allows one to fill the database with all the necessary information to implement the educational goals of literary tourism, taking into account the needs of users. Due to the ability to automatically retrieve the necessary information the designed and implemented information system significantly saves user’s time and financial resources for organizing tourism, as well as automatically collects and stores data about the regional attractions found, which confirms the relevance of the study and the need to develop the described system, which has successfully passed testing and approbation. Prospects for further development imply a complete transition to web technologies in order to further popularize this information system among a large user audience of students and teachers both in megacities and in the regions.

Modern Search Engine Techniques

To be at the top in the world of www, every website needs to be standardized and well format as per standards defined. SEO has many new algorithms & indexing ways that helps user to get best results for their searches. Search Engine Optimization (SEO) is important for websites to improve the rank for search results and get more page views resulting into large user traffic. The search engine ranks provide the better and optimized result to user's query, which help them to view the exact contents they are looking for from list of popular web pages among the number of pages available on the web. My case study will focus on some advanced techniques that are helpful to the website owner for better page rank in search result. This will focus on simple modern SEO techniques that can be add on to your website designing which will indirectly help into SEO page rank.

Data management and processing workflow for the Materials Physics and Engineering group beamlines at the Advanced Photon Source

The ability to store, organize, process and distribute experimental data effectively, efficiently and securely is particularly important for large user facilities like the Advanced Photon Source. In this article, the deployment of the APS Data Management System (DM) at the 1-ID and 6-BM beamlines of the APS is described. These two beamlines support a wide range of experimental techniques and generate data at relatively high rates, making them ideal candidates to illustrate the deployment and customization of the DM system and its tools. Using several usage examples at these beamlines, various capabilities of the DM system are described.