Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

A Review on Fact Extraction and Verification

We study the fact-checking problem, which aims to identify the veracity of a given claim. Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its accompanied dataset. The task consists of the subtasks of retrieving the relevant documents (and sentences) from Wikipedia and validating whether the information in the documents supports or refutes a given claim. This task is essential and can be the building block of applications such as fake news detection and medical claim verification. In this article, we aim at a better understanding of the challenges of the task by presenting the literature in a structured and comprehensive way. We describe the proposed methods by analyzing the technical perspectives of the different approaches and discussing the performance results on the FEVER dataset, which is the most well-studied and formally structured dataset on the fact extraction and verification task. We also conduct the largest experimental study to date on identifying beneficial loss functions for the sentence retrieval component. Our analysis indicates that sampling negative sentences is important for improving the performance and decreasing the computational complexity. Finally, we describe open issues and future challenges, and we motivate future research in the task.

Interstitial lung abnormalities: new insights between theory and clinical practice

Interstitial lung abnormalities (ILAs) represent radiologic abnormalities incidentally detected on chest computed tomography (CT) examination, potentially related to interstitial lung diseases (ILD). Numerous studies have demonstrated that ILAs are associated with increased risk of progression toward pulmonary fibrosis and mortality. Some radiological patterns have been proven to be at a higher risk of progression. In this setting, the role of radiologists in reporting these interstitial abnormalities is critical. This review aims to discuss the most recent advancements in understanding this radiological entity and the open issues that still prevent the translation from theory to practice, emphasizing the importance of ILA recognition and adequately reporting in clinical practice.

A Discrete Process Modelling and Simulation Methodology for Industrial Systems within the Concept of Digital Twins

A generic well-defined methodology for the construction and operation of dynamic process models of discrete industrial systems following a number of well-defined steps is introduced. The sequence of steps for the application of the method as well as the necessary inputs, conditions, constraints and the results obtained are defined. The proposed methodology covers the classical offline modelling and simulation applications as well as their online counterpart, which use the physical system in the context of digital twins, with extensive data exchange and interaction with sensors, actuators and tools from other scientific fields as analytics and optimisation. The implemented process models can be used for what-if analysis, comparative evaluation of alternative scenarios and for the calculation of key performance indicators describing the behaviour of the physical systems under given conditions as well as for online monitoring, management and adjustment of the physical industrial system operations with respect to given rules and targets. An application of the proposed methodology in a discrete industrial system is presented, and interesting conclusions arise and are discussed. Finally, the open issues, limitations and future extensions of the research are considered.

Techniques to detect terrorists/extremists on the dark web: a review

PurposeWith the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.Design/methodology/approachThis study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.FindingsBased on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.Originality/valueAt the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

Blockchain Open Issues, Research Gaps, and Road Map of Future Blockchain for 2030

The future is great for blockchain technology. Blockchain market size can grow from 3 billion USD to 39.7 billion USD by 2025. Thirty percent of blockchain projects might go live this year. There is another prediction that 90% of those projects might have a substitute solution. Twenty-five percent of Forbes Global 2000 might start implementing blockchain for improving digital trust. The implementations might not use tokenization, smart contracts, decentralized consensus, and other features. The latest solutions during COVID-19 lockdown are becoming the reference solutions for the blockchain initiatives. The number of enterprises that are part of the blockchain networks has significantly increased. On the other hand, long-term blockchain implementations are on hold. Future projects are focusing on creating a digital platform for post-pandemic scenarios. Private blockchains are becoming popular, and they will have a bigger market share compared to public blockchains. European countries are coming up with their cryptocurrency, and China is ready with its crypto yuan.