Detection of Radicalisation and Extremism Online: A Survey

Introduction: Due to the lack of regulation, the large volume of user-generated online content reflects more closely the offline world than official news sources. Therefore, social media platforms have become an attractive space for anyone seeking independent information. One of the main goals of this work is to clarify concepts such as Extremism and Collective Radicalisation, Social Media, Sentiments/Emotions/Opinions Analysis, as well as the combinations of all of them. Methods: The automatic identification of extremism and collective radicalisation requires sophisticated Natural Language Processing (NLP) methods and resources, especially those dealing with opinions, emotions or sentiment analysis. Text mining and knowledge extraction are also crucial, in particular, directed toward social media and micro-blogging. Results: The present document comprehends a study on theoretical material, focusing on the main concepts of the subject, including the main problems and challenges, from the different areas that compose online radicalisation research. Understanding and detecting extremism and collective radicalism online has a connection to sentiment analysis and opinion mining. There are many barriers to understanding extremism and collective radicalisation; one is to differentiate between who is really engaged in the process and who is just eventually talking about it. Conclusions: The other focus of this work is to find the best ways to identify extremism and collective radicalisation on the internet, using sentiment analysis and focusing on probabilistic methods to create an unsupervised and language-independent approach.

DNA Methylation in INA, NHLH2, and THBS4 Is Associated with Metastatic Disease in Renal Cell Carcinoma

The detection of DNA methylation in primary tumor tissues could be relevant for early stratification of aggressive renal cell carcinomas (RCCs) as a basis for future personalized adjuvant therapy. Methylated TCGA KIRC based candidate CpG loci in INA, NHLH2, and THBS4 that are possibly associated with RCC metastasis were evaluated by pyrosequencing in 154 paired normal adjacent and primary tumor tissues, as well as in 202 metastatic tissues. Statistical analysis was carried out by bivariate logistic regression for group comparisons, log rank survival analysis, and unsupervised and supervised analysis for the classification of tumors. Increased methylation of INA, NHLH2, and THBS4 loci were significantly associated with distant metastasis in primary tumors (p < 0.05), tissue-specific hypermethylation in metastatic (p = 7.88 × 10−8, 5.57 × 10−10, 2.06 × 10−7) and tumor tissues (p = 3.72 × 10−24, 3.17 × 10−13, 1.58 × 10−19), and shortened progression free survival in patients (p = 0.03). Combined use of CpG site-specific methylation permits the discrimination of tissues with metastatic disease and reveals a significant contribution of CpG sites in all genes to the statistical classification model. Thus, metastasis in RCC is significantly associated with methylation alterations in INA, NHLH2, and THBS4 loci, providing independent information for the potential early detection of aggressive renal cancers as a rationale for stratifying patients to adjuvant therapies.

Composite Quantile Probability Predictions: Performance and Coherence Analysis of US COVID-19 Confirmed Infection Cases

An analytical framework is presented for the evaluation of composite probability forecasts using empirical quantiles. The framework is demonstrated via the examination of forecasts of the changes in the number of US COVID-19 confirmed infection cases, applying 18 two-week ahead quantile forecasts from four forecasting organisations. The forecasts are analysed individually for each organisation and in combinations of organisational forecasts to ascertain the highest level of performance. It is shown that the relative error reduction achieved by combining forecasts depends on the extent to which the component forecasts contain independent information. The implications of the study are discussed, suggestions are offered for future research and potential limitations are considered.

Real-time and remote MCMC trace inspection with Beastiary

Bayesian phylogenetic methods have gained substantial popularity in the last decade, due to their ability to incorporate independent information and fit complex models. Most Bayesian implementations rely on Markov chain Monte Carlo (MCMC), which in turn requires careful interpretation of the output to assess the statistical validity of any resulting inferences. Here we describe Beastiary, a package for real-time and remote inspection of log flies generated by MCMC analysis commonly utilised in Bayesian phylogenetic analysis. Beastiary is an easily deployed web-sever that can be used to summarise and visualise the output of many popular software packages including BEAST, BEAST2, RevBayes, and MrBayes. We describe the overall design and implementation of Beastiary and some typical use cases, with a particular focus on the capability of monitoring analyses from remote servers.

Understanding the Emotion Coping Strategies During Public Emergencies – From the Perspective of Psychological Distance

Research has investigated behavioral coping strategies for the negative emotions that public emergencies elicit. Accordingly, our current research explored how people coped with negative emotions in response to the coronavirus disease (COVID-19) outbreak, from a cognitive perspective. Building on the theory of psychological distance and self-construal, we proposed that people who experienced fear, sadness and anxiety responded with independent-self construal, focusing on information that related to themselves and the novel virus (independent information). On the other hand, people who experienced fear, sadness and anger responded with interdependent-self construal, focusing on information that pertained to “us”, the virus and nature (interdependent information). We collected data from 1,142 participants at both the initial peak of the outbreak and when its spread had subsided. Based on this longitudinal data, we examined the effectiveness of these strategies, and our findings suggested that independent information was effective in decreasing fear and anxiety, while interdependent information effectively mitigated sadness. The findings could help researchers, practitioners, governments, and organizations to implement appropriate information strategies to regulate individuals’ negative emotions during and after the COVID-19 pandemic.

A formal pattern of information system design

A new design pattern intended for distributed cloud-based information systems is proposed. Pattern is based on the traditional client-server architecture. The server side is divided into three principal components: data storage, application server and cache server. Each component can be used to deploy parts of several independent information systems, thus realizing shared-resource approach. A strategy of separation of competencies between the client and the server is proposed. The strategy assumes that the client side is responsible for application logic and the server side is responsible for data storage consistency and data access control. Data protection is ensured by means of two particular approaches: at the entity level and at the transaction level. The application programming interface to access data is presented at the level of identified transaction descriptors.

Shape measures of generalized beta distributions

This paper presents shape measures for generalized beta distributions that unit many subfamilies of distributions. For the study of complex systems, the information entropy of the whole family of the generalized beta distribution is obtained. The paper uses the interval of entropy uncertainty as an estimate of the entropy uncertainty for probable models, which are given in units of an observable random variable. The entropy uncertainty interval was used to construct the entropy coefficient of unbiased subfamilies of the generalized beta distribution. Particular entropy coefficients are given for frequently used subfamilies of beta distribution, that greatly facilitates the use of coefficients as independent information measures in determining the shape of models. The paper contains the most general formulas for probabilistic measures of the distributions shape also.

Shape measures for the generalized beta exponential distribution

This paper contains parametric and informational shape measures for various families of the generalized beta exponential distribution since it is important to determination of the distribution shape for analysing an experimental data set. A logistic parameter is used to select independent types of beta exponential distributions, that it allows to combine the distributions of different subfamilies. In this paper the use of parametric shape measures to pre-define distribution shape is discusses. In particular, the initial and standard central moments for the main types of generalized beta exponential distribution are given. In the paper it is proposes to use the entropy coefficient of unshifted distribution as an independent information measure of the shape of unshifted generalized beta exponential distributions. In order to increase the reliability of the preliminary determination of the shape of the model, expressions for the entropy coefficient of shifted families both the generalized beta exponential distributions of the first and second types, and the generalized gamma exponential distribution were obtained. For practical applied the entropy coefficients of unshifted distributions for various subfamilies of generalized beta exponential distributions can be useful.

Measures of shape for the generalized beta exponential distribution

This paper contains parametric and informational measures of shape for various families of the generalized beta exponential distribution since it is important to determination of the distribution shape for analysing an experimental data set. A logistic parameter is used to select independent types of beta exponential distributions, that it allows to combine the distributions of different subfamilies. In this paper the use of parametric shape measures to predefine distribution shape is discusses. In particular, the initial and standard central moments for the main types of generalized beta exponential distribution are given. In the paper it is proposes to use the entropy coefficient of unshifted distribution as an independent information measure of the shape of unshifted generalized beta exponential distributions. In order to increase the reliability of the preliminary determination of the shape of the model, expressions for the entropy coefficient of shifted families both the generalized beta exponential distributions of the first and second types, and the generalized gamma exponential distribution were obtained. For practical applied the entropy coefficients of unshifted distributions for various subfamilies of generalized beta exponential distributions can be useful.

Disregarding the Shoulders of Giants: Inferences from Innovation Research

Studies proposing new determinants of corporate innovation include previously identified factors in an ad hoc manner. We find that only a sparse set of recently proposed innovation determinants provide material, independent information about patents and citations. We document that inferences in recent empirical studies often change when we include previously discovered innovation determinants. Commonly used econometric methods, including fixed effects and plausible shocks, do not always mitigate the need to condition on previously identified innovation determinants. Rather than randomly selecting a subset of control variables from prior studies, our analysis offers researchers a framework to consider previously proposed variables.