Event Detection and Tracking Detection of Dangerous Events on Social Media

Online social media platforms have become essential tools for communication and information exchange in our lives. It is used for connecting with people and sharing information. This phenomenon has been intensively studied in the past decade to investigate users’ sentiments for different scenarios and purposes. As the technology advanced and popularity increased, it led to the use of different terms referring to similar topics which often result in confusion. We study such trends and intend to propose a uniform solution that deals with the subject clearly. We gather all these ambiguous terms under the umbrella of the most recent and popular terms to reach a concise verdict. Many events have been addressed in recent works that cover only specific types and domains of events. For the sake of keeping things simple and practical, the events that are extreme, negative, and dangerous are grouped under the name Dangerous Events (DE). These dangerous events are further divided into three main categories of action-based, scenario-based, and sentiments-based dangerous events to specify their characteristics. We then propose deep-learning-based models to detect events that are dangerous in nature. The deep-learning models that include BERT, RoBERTa, and XLNet provide valuable results that can effectively help solve the issue of detecting dangerous events using various dimensions. Even though the models perform well, the main constraint of fewer available event datasets and lower quality of certain events data affects the performance of these models can be tackled by handling the issue accordingly.As plataformas online de redes sociais tornaram-se ferramentas essenciais para a comunicação, conexão com outros, e troca de informação nas nossas vidas. Este fenómeno tem sido intensamente estudado na última década para investigar os sentimentos dos utilizadores em diferentes cenários e para vários propósitos. Contudo, a utilização dos meios de comunicação social tornou-se mais complexa e num fenómeno mais vasto devido ao envolvimento de múltiplos intervenientes, tais como empresas, grupos e outras organizações. À medida que a tecnologia avançou e a popularidade aumentou, a utilização de termos diferentes referentes a tópicos semelhantes gerou confusão. Por outras palavras, os modelos são treinados segundo a informação de termos e âmbitos específicos. Portanto, a padronização é imperativa. O objetivo deste trabalho é unir os diferentes termos utilizados em termos mais abrangentes e padronizados. O perigo pode ser uma ameaça como violência social, desastres naturais, danos intelectuais ou comunitários, contágio, agitação social, perda económica, ou apenas a difusão de ideologias odiosas e violentas. Estudamos estes diferentes eventos e classificamos-los em tópicos para que a ténica de deteção baseada em tópicos possa ser concebida e integrada sob o termo Evento Perigosos (DE). Consequentemente, definimos o termo proposto “Eventos Perigosos” (Dangerous Events) e dividimo-lo em três categorias principais de modo a especificar as suas características. Sendo estes denominados Eventos Perigosos, Eventos Perigosos de nível superior, e Eventos Perigosos de nível inferior. O conjunto de dados MAVEN foi utilizado para a obtenção de conjuntos de dados para realizar a experiência. Estes conjuntos de dados são filtrados manualmente com base no tipo de eventos para separar eventos perigosos de eventos gerais. Os modelos de transformação BERT, RoBERTa, e XLNet foram utilizados para classificar dados de texto consoante a respetiva categoria de Eventos Perigosos. Os resultados demonstraram que o desempenho do BERT é superior a outros modelos e pode ser eficazmente utilizado para a tarefa de deteção de Eventos Perigosos. Salienta-se que a abordagem de divisão dos conjuntos de dados aumentou significativamente o desempenho dos modelos. Existem diversos métodos propostos para a deteção de eventos. A deteção destes eventos (ED) são maioritariamente classificados na categoria de supervisonado e não supervisionados, como demonstrado nos metódos supervisionados, estão incluidos support vector machine (SVM), Conditional random field (CRF), Decision tree (DT), Naive Bayes (NB), entre outros. Enquanto a categoria de não supervisionados inclui Query-based, Statisticalbased, Probabilistic-based, Clustering-based e Graph-based. Estas são as duas abordagens em uso na deteção de eventos e são denonimados de document-pivot and feature-pivot. A diferença entre estas abordagens é na sua maioria a clustering approach, a forma como os documentos são utilizados para caracterizar vetores, e a similaridade métrica utilizada para identificar se dois documentos correspondem ao mesmo evento ou não. Além da deteção de eventos, a previsão de eventos é um problema importante mas complicado que engloba diversas dimensões. Muitos destes eventos são difíceis de prever antes de se tornarem visíveis e ocorrerem. Como um exemplo, é impossível antecipar catástrofes naturais, sendo apenas detetáveis após o seu acontecimento. Existe um número limitado de recursos em ternos de conjuntos de dados de eventos. ACE 2005, MAVEN, EVIN são alguns dos exemplos de conjuntos de dados disponíveis para a deteção de evnetos. Os trabalhos recentes demonstraram que os Transformer-based pre-trained models (PTMs) são capazes de alcançar desempenho de última geração em várias tarefas de NLP. Estes modelos são pré-treinados em grandes quantidades de texto. Aprendem incorporações para as palavras da língua ou representações de vetores de modo a que as palavras que se relacionem se agrupen no espaço vectorial. Um total de três transformadores diferentes, nomeadamente BERT, RoBERTa, e XLNet, será utilizado para conduzir a experiência e tirar a conclusão através da comparação destes modelos. Os modelos baseados em transformação (Transformer-based) estão em total sintonia utilizando uma divisão de 70,30 dos conjuntos de dados para fins de formação e teste/validação. A sintonização do hiperparâmetro inclui 10 epochs, 16 batch size, e o optimizador AdamW com taxa de aprendizagem 2e-5 para BERT e RoBERTa e 3e-5 para XLNet. Para eventos perigosos, o BERT fornece 60%, o RoBERTa 59 enquanto a XLNet fornece apenas 54% de precisão geral. Para as outras experiências de configuração de eventos de alto nível, o BERT e a XLNet dão 71% e 70% de desempenho com RoBERTa em relação aos outros modelos com 74% de precisão. Enquanto para o DE baseado em acções, DE baseado em cenários, e DE baseado em sentimentos, o BERT dá 62%, 85%, e 81% respetivamente; RoBERTa com 61%, 83%, e 71%; a XLNet com 52%, 81%, e 77% de precisão. Existe a necessidade de clarificar a ambiguidade entre os diferentes trabalhos que abordam problemas similares utilizando termos diferentes. A ideia proposta de referir acontecimentos especifícos como eventos perigosos torna mais fácil a abordagem do problema em questão. No entanto, a escassez de conjunto de dados de eventos limita o desempenho dos modelos e o progresso na deteção das tarefas. A disponibilidade de uma maior quantidade de informação relacionada com eventos perigosos pode melhorar o desempenho do modelo existente. É evidente que o uso de modelos de aprendizagem profunda, tais como como BERT, RoBERTa, e XLNet, pode ajudar a detetar e classificar eventos perigosos de forma eficiente. Tem sido evidente que a utilização de modelos de aprendizagem profunda, tais como BERT, RoBERTa, e XLNet, pode ajudar a detetar e classificar eventos perigosos de forma eficiente. Em geral, o BERT tem um desempenho superior ao do RoBERTa e XLNet na detecção de eventos perigosos. É igualmente importante rastrear os eventos após a sua detecção. Por conseguinte, para trabalhos futuros, propõe-se a implementação das técnicas que lidam com o espaço e o tempo, a fim de monitorizar a sua emergência com o tempo

Average dynamical frequency behaviour for multi-area islanded micro-grid networks

A micro-grid is a part of power system which able to operates in grid or islanding mode. The most important variable that able to give us information about the stability in islanded micro-grid network is the frequency dynamical responses. The frequency analysis for multi-area micro-grid network model may involve a complicated of mathematical equations. This makes the researcher intending to omit several unnecessary parameters in order to simplify the equations. The purpose of this paper is to show an approach to derive the mathematical equations to represent the average behavior of frequency dynamical responses for two different micro-grid areas. Both of networks are assumed to have non-identical distributed generator behavior with different parameters. The prime mover and speed governor systems are augmented with the general swing equation. The tie line model and the information of rotor angle was considered. Then, in the last section, the comparison between this technique with the conventional approach using centre of inertia (COI) technique was defined

CLINICAL PRESENTATION AND LIFESTYLE RELATED RISK FACTORS OF BREAST CANCER AMONG DIFFERENT AGE AND ETHNIC GROUPS

Breast cancer (BC) is one of the most frequent and leading cause of malignancies in females globally. In Pakistan, breast cancer is most frequently found in younger individuals and late stage presentation is the key feature for clinical diagnosis. Numbers of genetic factors are reported to be significantly associated with the manifestation of breast cancer. A number of factors including gender, age, genetic predisposition, familial vertical history, ethnicity and life style eventually leading to the development of the cancer. Therefore, we identified the role of biochemical characteristics of all participants in the development of breast cancer. 50 breast cancer patients were enrolled in this study. A written informed consent was taken from each of the patients prior to data collection through questionnaire. People belonging to different ethnic groups: Pashtoon was found to be the highest noteworthy figure of breast cancer patients with an overall of 14 (28%) followed by Afghani ethnic group with 7 (14%), Baloch 15 (30%), Hazara 8 (16%), Punjabi 3 (6%) and Sindhi 3 (6%). Key words: Breast cancer, Ethnic groups, Cenar Hospital, Balochistan

Blooms of pollution indicator micro-alga (Synedra acus) in Northern Arabian Sea along Karachi, Pakistan

1377-1381Sewage water from Karachi and agricultural runoff from adjacent lands put lots of pollutants and nutrients in coastal waters of Karachi. Appearance of algal blooms in new geographical areas is being frequently reported and is attributed to increased marine pollution and climatic changes. For the current monitoring study, fortnightly phytoplankton samples were collected from polluted as well as cleaner localities along Karachi coast by using a phytoplankton net of 40μm mesh size. Standard parameters like Temperature, salinity, pH, rainfall were also recorded. Samples were analyzed qualitatively using phase-contrast microscope. Unusual, non-toxic and irregular blooms of a pollution indicator micro alga, Synedra acus has been frequently observed, suppressing existence of all other phytoplankton. This irregular appearance could not be related with any oceanographic parameters under study. These results reflect the presence of suspect factors (nutrients, environmental pollutants and climatic conditions) that might have governed synedra acus to bloom in this part of the northern Arabian Sea

Total Power Deficiency Estimation Of Isolated Power System Network Using Full-State Observer Method

An isolated electrical network with an independent local distributed generator is very sensitive towards the contingencies between load demand and supply. Although the network system has less complexity in term of structure, its stability condition is crucial due to its stand-alone operating condition. The total power deficit in the network gives the important information related to the dynamical frequency responses which may directly affect the system's stability level. In this paper, the approach to estimate the total power deficiency for the isolated electrical network was presented by utilized the Luenberger observer method. Although the power deficit is not the state variable in the network mathematical model, the solution of estimation problem was feasible by introducing the new variable using additional dummy system. The simulation was carried out by using MATLAB/Simulink environment and the designed estimator was verified using multifarious load demand changes. The results show that the estimated signal was successfully tracked the expected actual signal with minimum error

Detection of Extreme Sentiments on Social Networks with BERT

International audienc

An Investigation Of Inertia Constant In Single Generator On Transient Analysis For An Isolated Electrical Network System

An isolated electrical network which fed by an independent generator for a low voltage system is considerable in remote and islandic areas. Although the network system has less complexity in term of system structure, its stability level is crucial due to frequency dynamical responses. An influence of initial stability margin on frequency stability study during contingency situation is a thing rather than being ignored. Here the initial transient response inherently delivers important info such as system inertia and momentarily power deficit. In this paper, an investigation of transient stability responses under different inertia values is carried out. The investigation is carried out by modelling the isolated system in MATLAB/Simulink environment which consists of state-space mathematical equations. It is confirmed that the generator system inertia shapes the initial slope, speed droop and oscillation. For a verification purpose, the influence of system inertia is also analyzed using bode diagram in which system gain and frequency margin are evaluated

3-D seismic interpretation of stratigraphic and structural features in the Upper Jurassic to Lower Cretaceous sequence of the Gullfaks Field, Norwegian North Sea: A case study of reservoir development

Abstract The 3-D seismic dataset is a key tool to analyze and understand the mechanism of structural and stratigraphic hydrocarbon (HC) trapping in the subsurface. Conventionally used subsurface seismic characterization methods for fractures are based on the theory of effective anisotropy medium. The aim of this work is to improve the structural images with dense sampling of 3-D survey to evaluate structural and stratigraphic models for reservoir development to predict reservoir quality. The present study of the Gullfaks Field, located in the Norwegian North Sea Gullfaks sector, identifies the shallowest structural elements. The steepness of westward structural dip decreases eastward during the Upper Jurassic to Lower Cretaceous deposition. Reservoir sands consist of the Middle Jurassic deltaic deposits and Lower Jurassic fluvial channel and delta plain deposits. Sediment supply steadily prevails on sea-level rise and the succession displays a regressive trend indicated by a good continuous stacking pattern. The key factor for the development of reservoirs in the Gullfaks Field is fault transmissibility with spatially distributed pressure. The majority of mapped faults with sand-to-sand contacts are non-sealing, which provide restriction for the HC flow between the fault blocks. The traps for HC accumulation occur between the post-rift and syn-rift strata, i.e. antiform set by extensional system, unconformity trap at the top of syn-deposition, and structural trap due to normal faults. Overall reservoir quality in the studied area is generally excellent with average 35% porosity and permeability in the Darcy range. Our findings are useful to better understand the development of siliciclastic reservoirs in similar geological settings worldwide