Clickbait can be a spam or an advert which more often provides a link to a commercial website, or it might be a headline to a news media website which makes money from page views by providing eye-catchy headlines with deceptive news. This paper focuses on the latter one to identify clickbaits that use news headlines to publish news items in Twitter. In this work, we aimed to use Transfer Learning approaches by adding various configuration changes to the existing models in order to detect clickbaits. Based on author’s knowledge, this is the first attempt to adapt Transfer Learning to classify Clickbaits. The analysis in this work are mainly focused on three main models: BERT, XLNet and RoBERTa.We fine-tuned these models by integrating novel configuration changes to the default architecture such as model expansion, pruning and data augmentation strategies. We used Webis Clickbait Challenge 2017 dataset to train our models and it was introduced to evaluate the level of clickbait of a Twitter post. The best performed model at this competition is considered as the benchmark for this research. Our analyses were mainly focused on eight different scenarios after applying several fine-tuning approaches and model configuration changes. The results shown that, our proposed Transfer Learning approaches outperformed the considered benchmark. In our experiments, the best performed Transfer Learning model is RoBERTa with the integration of an additional non-linear layer to the output tensors extracted from hidden outputs. For binary classification, this configuration has achieved 19.12% more accuracy in compared to the benchmark model. There is no significant improvement when models expanded by adding extra RNN layer(s). Apart from that, we experimented with another labelled clickbait dataset (Kaggle clickbait challenge) to explore the performance of our fine-tuned models under different scenarios.

Recent years brought an exponential growth of social media which revolutionized freedom of speech but significantly increased the propagation of hate speech and hate-based activities. Therefore, constructive countermeasures are necessary to prevent escalating hateful content on online social media. Many recent works target explicit hate speech, but only a few studies have utilized multiple fused features such as sentiment, targets, and emotions as attributes to enhance the detection of hate speech. In general, sentiment features help to discern feelings such as positivity or negativity, and emotion features provide a deeper level of granularity, focusing on a more comprehensive understanding of sensitivities. The aim of this paper is to investigate the significance of incorporating fine-grained emotions as an essential feature in improving the classification of implicit hate speech. First, we analyzed emotion variations of hateful and non-hateful content and explored their major fine-grained emotion discrepancies targeting implicit hateful content. Next, we introduce a multi-task learning approach that integrates emotions and sentiment features to classify implicit expressions of hatred. To evaluate the effectiveness of our multi-task learning approach, we compared it with baseline models using single-task learning approaches. The experimental results show that our multi-task approach outperformed in classifying implicit hate speech compared to the baseline models and demonstrates that fine-grained emotional knowledge decreases the classification error across multiple implicit hate categories.

Pruning, as a technique to reduce the complexity and size of Transformer-based models, has gained significant attention in recent years. While various models have been successfully pruned, pruning BERT poses unique challenges due to their fine-grained structure and overparameterization. However, by carefully considering these factors, it is possible to prune BERT without significantly degrading its pre-trained loss. In this paper, we propose a Meta-learning-based pruning approach that can adaptively identify and eliminate insignificant attention weights. The performance of the proposed model is compared with several baseline models, as well as the default fine-tuned BERT model. The baseline pruning strategies employed low-level pruning techniques, targeting the removal of only 20% of the connections. The experimental results show that the proposed model outperforms the other baseline models, in terms of lower inference latency, higher MCC and lower loss. However, there is no significant improvement observed in terms of average FLOPs (floating-point operations per second). Furthermore, we conduct a comparative evaluation of the baseline models and our proposed model using two explainable (XAI) approaches. While other models allocate reasonable attention to less significant words for sentiment classification, our model assigns higher probabilities to the most significant sentimental words.

Traffic flow prediction is an important feature for smart cities, as it helps in implementing effective traffic policies. However, accurate prediction and successful traffic management rely on reliable traffic information, which may not always be available due to the deployment and management costs of a specialized traffic intensity sensor infrastructure. We investigate the possibility of collecting alternative data sources and employing data fusion methodologies to build usable measurements. Hence, in this research, we focus on leveraging Artificial Intelligence techniques, data fusion, and synthetic sensing, to improve the accuracy of traffic flow prediction in regions with limited sensor infrastructure. The considered alternative measurements are source-destination data sets (e.g., those provided by mobile maps providers), meteorological data, bus trajectory information, and path and delay, obtained from metropolitan transport service providers. By fusing and analyzing these data sources, it becomes possible to predict traffic flow in a specific and localized area. In this research, our focus is specifically on the city of Issy le Moulineax, France. The study has analyzed the fused data sets using three distinct machine learning techniques, LSTM, FB Prophet, and Neural Prophet, to identify the most suitable model. The goal is to help cities have access to accurate traffic flow predictions without the need to deploy a specialized traffic sensor network. These data can then be used to facilitate urban mobility planning.

The Internet of Things (IoT) consists of a myriad of smart devices and offers tremendous innovation opportunities in industry, homes, and businesses to enhance the productivity and the quality of life. However, ecosystem of infrastructures and the services associated with IoT devices have introduced a new set of vulnerabilities and threats, resulting in abnormal values of information collected by sensors, jeopardizing system security. To secure sensor networks, it must be possible to detect such anomalies or sequences of patterns in IoT devices that significantly deviate from normal behavior. To perform this task, this paper proposes a real-time streaming anomaly detection method based on a Bloom filter combined with hashing. This method expands the data dimensions through a hashing algorithm, and then adopts competitive learning (Winner-Take-All) to build a multi-layer Bloom Filter anomaly detection model. The feasibility of the proposed algorithm is verified theoretically using two datasets, KDD (to detect anomalies at the TCP/IP network level) and Credit (to detect anomalies during credit card transactions). The simulation results show that the proposed in this paper can effectively identify anomalies in the simulation data streams, with almost 95% accuracy for both datasets.