Promoting Resiliency in Emergency Communication Networks: A Network Interdiction Stylized Initial Case Study Model of a Miami-Dade County Network

Police, fire, and emergency personnel rely on wireless networks to serve the public. Whether it is during a natural disaster, or just an ordinary calendar day, wireless nodes of varying types form the infrastructure that local, regional, and even national scale agencies use to communicate while keeping the population served safe and secure. We present a network interdiction modeling approach that can be utilized for analyzing vulnerabilities in public service wireless networks subject to hacking, terrorism, or destruction from natural disasters. We develop a case study for the wireless network utilized by the sheriff’s department of Miami-Dade County in Florida in the United States. Our modeling approach, given theoretical budgets for the “hardening” of wireless network nodes and for would-be destroyers of such nodes, highlights parts of the network where further investment may prevent damage and loss of capacity

Credibility assessment of financial stock tweets

© 2020 The Authors Social media plays an important role in facilitating conversations and news dissemination. Specifically, Twitter has recently seen use by investors to facilitate discussions surrounding stock exchange-listed companies. Investors depend on timely, credible information being made available in order to make well-informed investment decisions, with credibility being defined as the believability of information. Much work has been done on assessing credibility on Twitter in domains such as politics and natural disaster events, but the work on assessing the credibility of financial statements is scant within the literature. Investments made on apocryphal information could hamper efforts of social media's aim of providing a transparent arena for sharing news and encouraging discussion of stock market events. This paper presents a novel methodology to assess the credibility of financial stock market tweets, which is evaluated by conducting an experiment using tweets pertaining to companies listed on the London Stock Exchange. Three sets of traditional machine learning classifiers (using three different feature sets) are trained using an annotated dataset. We highlight the importance of considering features specific to the domain in which credibility needs to be assessed for – in the case of this paper, financial features. In total, after discarding non-informative features, 34 general features are combined with over 15 novel financial features for training classifiers. Results show that classifiers trained on both general and financial features can yield improved performance than classifiers trained on general features alone, with Random Forest being the top performer, although the Random Forest model requires more features (37) than that of other classifiers (such as K-Nearest Neighbours − 9) to achieve such performance

What is Risk Culture, and Why Do We Need It?

Hybrid threats dominate our contemporary and technological society, and new risks can be challenging to predict because of today’s digitization. As modern safety-critical organizations and their technologies become more complex, similar to the digitalization experienced in today’s society, they become more susceptible to accidents resulting from unforeseen events. Thus, the importance of a sound and functional safety culture is deemed important. This thesis seeks to shed light on risk culture in safety-critical organizations, and whether focusing on risk culture could have a positive impact on safety culture and, subsequently, safety. Therefore, the problem statement of this thesis is: In what way can a sound risk culture improve an already existing safety culture in safety-critical organizations operating within compliance-based safety regimes? A qualitative research method, consisting of an exploratory case study, was used to help answer the problem statement. This included interviews with the case organization, as well as document analysis of both internal and external documents. In total, 18 semi-structured interviews with informants from different levels within the organization were conducted. After the interviews, the informants were provided with a statement form as a part of the interviews. Here, they were asked to rank eight statements from 1 to 5 as to whether they agreed or disagreed. The document analysis consisted of five internal documents and one external. While no organization can completely eliminate all risks, an emphasis on risk culture involves having a proactive and systematic approach to identifying, assessing, and managing risks. This could, in turn, ensure that decisions made are more likely to be the same, independently of the decision-maker. The findings of this thesis indicate that an emphasis on risk culture, as a component within safety culture, could have a positive impact and thus improve safety. We observed that there was a lack of a collective understanding of what risk, risk culture, and risk-based approach are, within the organization. An increased understanding of risk culture among individuals can contribute to a better systems-based understanding of how all tasks are interrelated and, thus, enhance awareness of the risks the employees may encounter in their workday. Consequently, emphasizing risk culture in light of their safety culture could have a positive influence on the existing safety culture, thus improving the level of safety

Automated Assessment of the Aftermath of Typhoons Using Social Media Texts

Disasters are one of the major threats to economics and human societies, causing substantial losses of human lives, properties and infrastructures. It has been our persistent endeavors to understand, prevent and reduce such disasters, and the popularization of social media is offering new opportunities to enhance disaster management in a crowd-sourcing approach. However, social media data is also characterized by its undue brevity, intense noise, and informality of language. The existing literature has not completely addressed these disadvantages, otherwise vast manual efforts are devoted to tackling these problems. The major focus of this research is on constructing a holistic framework to exploit social media data in typhoon damage assessment. The scope of this research covers data collection, relevance classification, location extraction and damage assessment while assorted approaches are utilized to overcome the disadvantages of social media data. Moreover, a semi-supervised or unsupervised approach is prioritized in forming the framework to minimize manual intervention. In data collection, query expansion strategy is adopted to optimize the search recall of typhoon-relevant information retrieval. Multiple filtering strategies are developed to screen the keywords and maintain the relevance to search topics in the keyword updates. A classifier based on a convolutional neural network is presented for relevance classification, with hashtags and word clusters as extra input channels to augment the information. In location extraction, a model is constructed by integrating Bidirectional Long Short-Time Memory and Conditional Random Fields. Feature noise correction layers and label smoothing are leveraged to handle the noisy training data. Finally, a multi-instance multi-label classifier identifies the damage relations in four categories, and the damage categories of a message are integrated with the damage descriptions score to obtain damage severity score for the message. A case study is conducted to verify the effectiveness of the framework. The outcomes indicate that the approaches and models developed in this study significantly improve in the classification of social media texts especially under the framework of semi-supervised or unsupervised learning. Moreover, the results of damage assessment from social media data are remarkably consistent with the official statistics, which demonstrates the practicality of the proposed damage scoring scheme