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Extractive text summarization has been a popular research area for many years. The goal of this task is to generate a compact and coherent summary of a given document, preserving the most important information. However, current extractive summarization methods still face several challenges such as semantic drift, repetition, redundancy, and lack of coherence. A novel approach is presented in this paper to improve the performance of an extractive summarization model based on bidirectional encoder representations from transformers (BERT) by incorporating topic modeling using the BERTopic model. Our method first utilizes BERTopic to identify the dominant topics in a document and then employs a BERT-based deep neural network to extract the most salient sentences related to those topics. Our experiments on the cable news network (CNN)/daily mail dataset demonstrate that our proposed method outperforms state-of-the-art BERT-based extractive summarization models in terms of recall-oriented understudy for gisting evaluation (ROUGE) scores, which resulted in an increase of 32.53% of ROUGE-1, 47.55% of ROUGE-2, and 16.63% of ROUGE-L when compared to baseline BERT-based extractive summarization models. This paper contributes to the field of extractive text summarization, highlights the potential of topic modeling in improving summarization results, and provides a new direction for future research.

Phishing attacks seriously threaten information privacy and security within the Internet of Things (IoT) ecosystem. Numerous phishing attack detection solutions have been developed for IoT; however, many of these are either not optimally efficient or lack the lightweight characteristics needed for practical application. This paper proposes and optimizes a lightweight deep-learning model for phishing attack detection. Our model employs a two-fold optimization approach: first, it utilizes the analysis of the variance (ANOVA) F-test to select the optimal features for phishing detection, and second, it applies the Cuckoo Search algorithm to tune the hyperparameters (learning rate and dropout rate) of the deep learning model. Additionally, our model is trained in only five epochs, making it more lightweight than other deep learning (DL) and machine learning (ML) models. The proposed model achieved a phishing detection accuracy of 91%, with a precision of 92% for the ’normal’ class and 91% for the ‘attack’ class. Moreover, the model’s recall and F1-score are 91% for both classes. We also compared our approach with traditional DL/ML models and past literature, demonstrating that our model is more accurate. This study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection.

During extreme space weather events, transient geomagnetic disturbances initiated by solar eruptive activities can induce geomagnetically induced currents (GICs), which have severe impacts on power grid systems and oil/gas pipelines. Observations indicate that GICs in power grids are characterized by large fluctuation amplitudes, broad frequency ranges, and significant randomness. Their behavior is influenced by several factors, including the sources of space weather disturbance, Earth’s electrical conductivity distribution, the structural integrity and performance of power grid equipment, and so on. This paper presents a hybrid prediction using actual GIC data from power grids and deep learning techniques. We employ various technical methods, including complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, to investigate short-term prediction methods for local grid GICs. The study uses GIC monitoring samples from 8 November 2004 for model training and testing. The results are evaluated using the coefficient of determination R2, root mean square error (RMSE), and mean absolute error (MAE). Preliminary research suggests that the combined CEEMDAN–CNN–LSTM–attention model significantly improves prediction accuracy and reduces the time delay in GIC prediction during geomagnetic storms compared to using LSTM neural networks alone.

The media ignores most conflicts most of the time. The coverage of the pre- and post-violence phases is negligible at best and only a few armed conflicts are covered in the violence phase. As focus and funds follow the cameras, the 1990s have witnessed a transfer of resources from more cost-effective, long-term efforts directed at preventing violent conflict and rebuilding war-torn societies to short-term emergency relief. Selective media coverage also contributes to an irrational allocation of short-term emergency relief because coverage is determined by factors other than humanitarian need. This invisible and indirect media impact on Western conflict management is far greater than the direct impact on intervention and withdrawal decisions that the debate over the CNN effect focuses on.

This article details the results of a plausibility probe of a policy-media interaction model designed to identify instances of media influence. If sufficient evidence is found to support the model, it can be used as part of a wider study examining the impact of media coverage on decisions to intervene during humanitarian crisis, the so-called CNN effect. The model predicts media influence when policy is uncertain and media coverage is framed so as to be critical of government and empathizes with suffering people. In order to test the model, it is applied to two cases: US intervention in Bosnia in 1995 in order to defend the Gorazde 'safe area' and Operation Allied Force in Kosovo in 1999. In the first case, the model highlights the impact of critical, empathizing media coverage and policy uncertainty in effecting the US decision to defend the Gorazde 'safe area'. In the second case, the failure of critical newspaper coverage to change the Clinton Administration's air-war policy highlights the limits of media influence when there exists policy certainty. Overall, it is argued that the plausibility probe supports the prediction that media influence occurs when policy is uncertain and media coverage is critically framed and empathizes with suffering people. And that when policy is certain, media influence is unlikely to occur. As such, the policy-media interaction model should prove a useful tool in testing the claim that media coverage causes intervention during humanitarian crisis.

This project advances the existing theoretical work on the CNN effect, a claim that innovations in the speed and quality of technology create conditions in which the media acts as an independent factor with significant influence. It provides a novel interpretation of the factors that drove Western policy towards military intervention in this area.

This article discusses using students' analysis of media coverage of the March 2011 Japanese earthquake, tsunami and nuclear events to develop their knowledge and understanding of geological concepts and related socio-scientific issues. It draws on news reported at that time, identifies themes in those reports, and suggests how this event can be used as a context for learning and teaching part of the Australian curriculum (science) in Year 6 and Year 9. Critical analysis of the media coverage enhances students' scientific literacy, in particular synthesis, evaluation and analysis. Ideas for classroom implementation are discussed. [Author abstract]

Dec. 4 (Bloomberg) –- For its 85th anniversary, Bloomberg Businessweek chronicles the most disruptive ideas of the past 85 years. In 1980, Cable News Network, the first 24-hour news channel, makes its debut. cand Lois Hart anchored CNN's first newscast. (Video by Brandon Lisy. Music by Andy Clausen) (Source: Bloomberg).

The CNN Effect examines the relationship between the state and its media, and considers the role played by the news reporting in a series of 'humanitarian' interventions in Iraq, Somalia, Bosnia, Kosovo and Rwanda. Piers Robinson challenges traditional views of media subservience and argues that sympathetic news coverage at key moments in foreign crises can influence the response of Western governments. 'A carefully crafted and thorough presentation … clear and insightful …Robinson has offered a thorough and thoughtful analysis.' – Political Communication