Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning

Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of scalable tools to perform analysis over a large number of events. In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated with the safety incident. Precursors are valuable to systems health and safety monitoring and in explaining and forecasting safety incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal behavior. We propose an approach by combining multiple-instance learning (MIL) and deep recurrent neural networks (DRNN) to take advantage of MIL's ability to learn using weakly supervised data and DRNN's ability to model temporal behavior. We describe the algorithm, the data, the intuition behind taking a MIL approach, and a comparative analysis of the proposed algorithm with baseline models. We also discuss the application to a real-world aviation safety problem using data from a commercial airline company and discuss the model's abilities and shortcomings, with some final remarks about possible deployment directions

Approaches to the solution to the problem of news-based events forecasting

An overview of the areas of application of approaches and methods of forecasting events based on past events. The substantiation of urgency of a theme is given and possibilities concerning application of results of work are resulted. Requirements for incoming news regarding their quality are defined. It is noted that there are four key criteria for the quality of the media, which are often two-component, namely: the relevance of news, providing the context in which the event, compliance with professional standards and a variety of materials. The key stages of working with data in order to obtain knowledge from them for forecasting events are identified. These include pre-processing of data (reduction to a standardized view that will understand and be able to process the algorithm), their analysis and the forecasting process itself. The spheres of application of associative series and Markov processes for search of causal relations, and time series for definition of the period of occurrence of an event with the set probability are specified

Approaches to the solution to the problem of news-based events forecasting

An overview of the areas of application of approaches and methods of forecasting events based on past events. The substantiation of urgency of a theme is given and possibilities concerning application of results of work are resulted. Requirements for incoming news regarding their quality are defined. It is noted that there are four key criteria for the quality of the media, which are often two-component, namely: the relevance of news, providing the context in which the event, compliance with professional standards and a variety of materials. The key stages of working with data in order to obtain knowledge from them for forecasting events are identified. These include pre-processing of data (reduction to a standardized view that will understand and be able to process the algorithm), their analysis and the forecasting process itself. The spheres of application of associative series and Markov processes for search of causal relations, and time series for definition of the period of occurrence of an event with the set probability are specified. Ref. 7, pic. 2

News event prediction using causality approach on South China Sea conflict

South China Sea (SCS) generates huge economic value in fishing and shipping lane as well as a high amount of natural resources. Due to its strategic location and high revenue generated, SCS became a place where several nearby countries competed for its territorial claims. Famous territorial disputes such as Spratly islands, Paracel island, Scarborough Shoal happened due to claim on SCS wealth. Newspapers are the main medium that disseminate the message to the public and update whenever SCS conflict happens. News related to SCS events or conflicts usually contain causal relationships between cause and effect. This causal relationship can be extracted and analyzed to obtain the trends of events and conflicts that have happened. In order to avoid any inevitable conflict among countries in SCS region, event prediction is important as it gives a better insight and foresee future events that might happen. In this paper, phrase similarity is used as important metrics for prediction models. First, it extracts news articles based on causality connectors such as "because", "after", "lead to", etc. into [removed] tuple. Then, three different embedding techniques, Doc2vec, InferSent and BERT were evaluated based on their best similarity score. The selected embedding technique is used to construct the prediction model and predict South China Sea conflict related events. A crude prediction is done based on similarity of past causes. The result shows that BERT has the highest average accuracy of 50.85% in getting the most similar phrase. By using the causal prediction model, a future possible event can be predicted and this helps to increase the awareness of national security among SCS nearby countries

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

Інформаційна система аналізу змісту новин та прогнозування подій на його основі

Пояснювальна записка магістерської дисертації складається з чотирьох розділів, містить 4 таблиці, 4 додатки та 43 джерела – загалом 128 сторінки. Об`єкт дослідження: зміст новин. Мета магістерської дисертації: підвищення релевантності прогнозів виникнення подій на основі аналізу змісту новин. Предмет дослідження – інформаційна система аналізу змісту новин та прогнозування подій. Методи дослідження – методи кластеризації та методи машинного навчання засновані на асоціативних правилах. Наукова новизна отриманих результатів полягає в розробці та модифікації підходів до прогнозування подій на основі новин, зокрема модифікації асоціативних правил щодо об’єднання їх в ланцюги, які дозволили б виявляти причинно-наслідкові зв’язки в тексті новин. Результати досліджень буде опубліковано в міжвідомчому науково-технічному збірнику «Адаптивні Системи Автоматичного Управління» (на стадії друку) [1] та в тезах наукової конференції студентів науково-практичної конференції молодих вчених та студентів «Інженерія програмного забезпечення і передові інформаційні технології» – SoftTech-2021 [2].The explanatory note of the master's dissertation consists of four sections, contains 4 tables, 4 appendices and 43 sources – a total of 128 pages. Object of research: news content. The purpose of the master's dissertation: to increase the relevance of forecasts of events based on the analysis of news content. The subject of research is the information system of news content analysis and event forecasting. Research methods - clustering methods and machine learning methods based on associative rules. The scientific novelty of the obtained results is the development and modification of approaches to forecasting events based on news, in particular the modification of associative rules for combining them into chains, which would reveal the causal links in the news text. The research results will be published in the interdepartmental scientific and technical collection "Adaptive Automatic Control Systems" (in print) [1] and in the abstracts of the scientific conference of students of the scientific-practical conference of young scientists and students "Software Engineering and Advanced Information Technology" - SoftTech- 2021 [2]