On the Aggression Diffusion Modeling and Minimization in Twitter

Aggression in online social networks has been studied mostly from the perspective of machine learning, which detects such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another is an important research topic, since it can enable effective aggression monitoring, especially in media platforms, which up to now apply simplistic user blocking techniques. In this article, we address aggression propagation modeling and minimization in Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade ( IC ) and Linear Threshold ( LT ), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as seed users selection, graph edge weighting, users’ activation timing, and so on. It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles’ overlaps, and activate users according to their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC = 0.89 in this prediction task. Finally, we investigate aggression minimization by launching competitive cascades to “inform” and “heal” aggressors. We show that IC and LT models can be used in aggression minimization, providing less intrusive alternatives to the blocking techniques currently employed by Twitter.

Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Privacy and confidentiality are very important prerequisites for applying process mining to comply with regulations and keep company secrets. This article provides a foundation for future research on privacy-preserving and confidential process mining techniques. Main threats are identified and related to a motivation application scenario in a hospital context as well as to the current body of work on privacy and confidentiality in process mining. A newly developed conceptual model structures the discussion that existing techniques leave room for improvement. This results in a number of important research challenges that should be addressed by future process mining research.

River Segmentation of Remote Sensing Images Based on Composite Attention Network

River segmentation of remote sensing images is of important research significance and application value for environmental monitoring, disaster warning, and agricultural planning in an area. In this study, we propose a river segmentation model in remote sensing images based on composite attention network to solve the problems of abundant river details in images and the interference of non-river information including bridges, shadows, and roads. To improve the segmentation efficiency, a composite attention mechanism is firstly introduced in the central region of the network to obtain the global feature dependence of river information. Next, in this study, we dynamically combine binary cross-entropy loss that is designed for pixel-wise segmentation and the Dice coefficient loss that measures the similarity of two segmentation objects into a weighted one to optimize the training process of the proposed segmentation network. The experimental results show that compared with other semantic segmentation networks, the evaluation indexes of the proposed method are higher than those of others, and the river segmentation effect of CoANet model is significantly improved. This method can segment rivers in remote sensing images more accurately and coherently, which can meet the needs of subsequent research.

Approximations of Fuzzy Numbers by Using r-s Piecewise Linear Fuzzy Numbers Based on Weighted Metric

Using simple fuzzy numbers to approximate general fuzzy numbers is an important research aspect of fuzzy number theory and application. The existing results in this field are basically based on the unweighted metric to establish the best approximation method for solving general fuzzy numbers. In order to obtain more objective and reasonable best approximation, in this paper, we use the weighted distance as the evaluation standard to establish a method to solve the best approximation of general fuzzy numbers. Firstly, the conceptions of I-nearest r-s piecewise linear approximation (in short, PLA) and the II-nearest r-s piecewise linear approximation (in short, PLA) are introduced for a general fuzzy number. Then, most importantly, taking weighted metric as a criterion, we obtain a group of formulas to get the I-nearest r-s PLA and the II-nearest r-s PLA. Finally, we also present specific examples to show the effectiveness and usability of the methods proposed in this paper.

Twenty Important Research Questions in Microbial Exposure and Social Equity

Social and political policy, human activities, and environmental change affect the ways in which microbial communities assemble and interact with people. These factors determine how different social groups are exposed to beneficial and/or harmful microorganisms, meaning microbial exposure has an important socioecological justice context.

Personalisation of DAPT: from ‘bench’ to bedside

The ideal duration of antiplatelet therapy following percutaneous coronary intervention is a topic of much discussion within the cardiology community. Numerous contemporary studies have highlighted benefits of both shortened and prolonged treatment courses within particular patient cohorts. Despite this, there is a delay in seeing such individualised decisions being made within stretched healthcare systems. We aimed to highlight this issue and facilitate discussions on how we can bring this important research from ‘bench’ to ‘bedside’.

Public Health Approaches to Reducing Community Gun Violence

Successful public health efforts are data-driven, focused on unhealthy or unsafe environments as well as risky behaviors, and often intentional about reforming systems that are unjust and harm public safety. While laws and their enforcement can be important to advance public health and safety, including reducing gun violence, minimizing harms of exposure to the criminal justice system is also important. Research demonstrates that appropriately targeted efforts that invest in and support individuals and neighborhoods at greatest risk for involvement in gun violence can be successful in saving lives and reaping impressive return on investment.

Formaldehyde Gas Sensor with Extremely High Response Employing Cobalt-Doped SnO2 Ultrafine Nanoparticles

Formaldehyde is a common carcinogen in daily life and harmful to health. The detection of formaldehyde by a metal oxide semiconductor gas sensor is an important research direction. In this...

The goal of psychological intervention and performed functions of psychologists as an aspect of the professional identity of psychologists: Latvian sample

Professional identity is formed by combination of several aspects, including professional practice, which for psychologists relates to psychological intervention and performed functions. The exclusive goal of psychological intervention and exclusively performed functions of psychologists are an important research subjects because more and more practicing professionals are performing activities linked to psychological intervention. The purpose of this paper was to investigate the exclusive goal of psychological intervention and exclusively performed functions of Latvian psychologists. Data was collected through an online survey developed in the framework of the international project “The Identity of Psychology” [1]. Three open-answer questions were used. Sample consisted of 54 Latvian psychologists of which 91% were women (n = 49) and 9 % were men (n = 5) aged from 25 to 60 years (M = 44.94; SD = 8.98). Data was analysed using thematic categorical content analysis. Findings indicate clear diversity in the opinion about the professional practice among Latvian psychologists. The results of this study indicate limited agreement and comprehension of Latvian psychologists about their professional practice which leads to necessity for further research to investigate the causes of such outcome to determine solutions on how to strengthen their professional practice and, more broadly, professional identity.