Redundancy reduction plays a critical role in optimizing sensor network performance. This research proposes a deep-learning approach to identify and eliminate redundant sensors in a traffic network. This strategy aims to create a more cost-effective, efficient and reliable traffic monitoring system, ultimately leading to improvements in the transportation infrastructure. Leveraging traffic data from the Madrid Open Data Portal (focusing on ’District 19’), we employed sensor correlation (cosine) and similarity analysis (VGG16-based model) to identify significant correlations among sensors. This allows for accurate prediction (using Long Short-Term Memory(LSTM)-based models) of values from highly correlated sensors, leading to a potential reduction in District 19’s sensor nodes by 43% (from 32 to 18) and connectivity edges by 82% (from 106 to 19). Notably, the predictive accuracy for ’highly similar’ sensors achieved an average R-squared score of 0.82, validating the reliability of LSTM model predictions. These initial results encourage a larger analysis of the methodology to better prove the potential of our deep learning approach in optimizing and streamlining smart city infrastructure. This promising approach can be extended to analyze districts with higher sensor density and be adapted for application in other cities. We aim to utilize deep learning algorithms to optimize future sensor deployment planning.

The rise of emergence of social media platforms has fundamentally altered how people communicate, and among the results of these developments is an increase in online use of abusive content. Therefore, automatically detecting this content is essential for banning inappropriate information, and reducing toxicity and violence on social media platforms. The existing works on hate speech and offensive language detection produce promising results based on pre-trained transformer models, however, they considered only the analysis of abusive content features generated through annotated datasets. This paper addresses a multi-task joint learning approach which combines external emotional features extracted from another corpora in dealing with the imbalanced and scarcity of labeled datasets. Our analysis are using two well-known Transformer-based models, BERT and mBERT, where the later is used to address abusive content detection in multi-lingual scenarios. Our model jointly learns abusive content detection with emotional features by sharing representations through transformers' shared encoder. This approach increases data efficiency, reduce overfitting via shared representations, and ensure fast learning by leveraging auxiliary information. Our findings demonstrate that emotional knowledge helps to more reliably identify hate speech and offensive language across datasets. Our hate speech detection Multi-task model exhibited 3% performance improvement over baseline models, but the performance of multi-task models were not significant for offensive language detection task. More interestingly, in both tasks, multi-task models exhibits less false positive errors compared to single task scenario.

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.

Video anomaly detection has always been a hot topic and attracting an increasing amount of attention. Much of the existing methods on video anomaly detection depend on processing the entire video rather than considering only the significant context. This paper proposes a novel video anomaly detection method named COVAD, which mainly focuses on the region of interest in the video instead of the entire video. Our proposed COVAD method is based on an auto-encoded convolutional neural network and coordinated attention mechanism, which can effectively capture meaningful objects in the video and dependencies between different objects. Relying on the existing memory-guided video frame prediction network, our algorithm can more effectively predict the future motion and appearance of objects in the video. Our proposed algorithm obtained better experimental results on multiple data sets and outperformed the baseline models considered in our analysis. At the same time we improve a visual test that can provide pixel-level anomaly explanations.

With the freedom of communication provided in online social media, hate speech has increasingly generated. This leads to cyber conflicts affecting social life at the individual and national levels. As a result, hateful content classification is becoming increasingly demanded for filtering hate content before being sent to the social networks. This paper focuses on classifying hate speech in social media using multiple deep models that are implemented by integrating recent transformer-based language models such as BERT, and neural networks. To improve the classification performances, we evaluated with several ensemble techniques, including soft voting, maximum value, hard voting and stacking. We used three publicly available Twitter datasets (Davidson, HatEval2019, OLID) that are generated to identify offensive languages. We fused all these datasets to generate a single dataset (DHO dataset), which is more balanced across different labels, to perform multi-label classification. Our experiments have been held on Davidson dataset and the DHO corpora. The later gave the best overall results, especially F1 macro score, even it required more resources (time execution and memory). The experiments have shown good results especially the ensemble models, where stacking gave F1 score of 97% on Davidson dataset and aggregating ensembles 77% on the DHO dataset.

Social networking sites (SNSs) facilitate the sharing of ideas and information through different types of feedback including publishing posts, leaving comments and other type of reactions. However, some comments or feedback on SNSs are inconsiderate and offensive, and sometimes this type of feedback has a very negative effect on a target user. The phenomenon known as flaming goes hand-in-hand with this type of posting that can trigger almost instantly on SNSs. Most popular users such as celebrities, politicians and news media are the major victims of the flaming behaviors and so detecting these types of events will be useful and appreciated. Flaming event can be monitored and identified by analyzing negative comments received on a post. Thus, our main objective of this study is to identify a way to detect flaming events in SNS using a sentiment prediction method. We use a deep Neural Network (NN) model that can identity sentiments of variable length sentences and classifies the sentiment of SNSs content (both comments and posts) to discover flaming events. Our deep NN model uses Word 2Vec and FastText word embedding methods as its training to explore which method is the most appropriate. The labeled dataset for training the deep NN is generated using an enhanced lexicon based approach. Our deep NN model classifies the sentiment of a sentence into five classes: Very Positive, Positive, Neutral, Negative and Very Negative. To detect flaming incidents, we focus only on the comments classified into the Negative and Very Negative classes. As a use-case, we try to explore the flaming phenomena in the news media domain and therefore we focused on news items posted by three popular news media on Facebook (BBCNews, CNN and FoxNews) to train and test the model. The experimental results show that flaming events can be detected with our proposed approach, and we explored main characteristics that trigger a flaming event and topics discussed in the flaming posts.

The rising popularity of social media has radically changed the way news content is propagated, including interactive attempts with new dimensions. To date, traditional news media such as newspapers, television and radio have already adapted their activities to the online news media by utilizing social media, blogs, websites etc. This paper provides some insight into the social media presence of worldwide popular news media outlets. Despite the fact that these large news media propagate content via social media environments to a large extent and very little is known about the news item producers, providers and consumers in the news media community in social media. To better understand these interactions, this work aims to analyze news items in two large social media, Twitter and Facebook. Towards that end, we collected all published posts on Twitter and Facebook from 48 news media to perform descriptive and predictive analyses using the dataset of 152K tweets and 80K Facebook posts. We explored a set of news media that originate content by themselves in social media, those who distribute their news items to other news media and those who consume news content from other news media and/or share replicas. We propose a predictive model to increase news media popularity among readers based on the number of posts, number of followers and number of interactions performed within the news media community. The results manifested that, news media should disperse their own content and they should publish first in social media in order to become a popular news media and receive more attractions to their news items from news readers.

Content originality detection is an interesting research topic in large-scale scenarios especially in social media where anyone has the ability to produce and disseminate content in different forms through their profiles and activities. What is missing in these communication sites is to be able to identify original content producers as some users spread information copied from other users without indicating its original producer, or where they found it. This paper provides a conceptualized approach for content originality detection and illustrates the efficiency of the model when applying it to a Twitter dataset. This approach amalgamates user's linguistic features and their online circadian behaviors to identify accurately the content originator for a given text. The proposed approach is evaluated using an F1-measure and the results indicate an accuracy of 95% or higher for all test scenarios. While achieving high accuracy in the test results, our approach, as a usecase, was applied in the context of news agencies popular worldwide to identify news producers and consumers by analyzing their Tweets. We investigated intra and inter news flows among several major news agencies considered in our dataset. Our results show that this proposed approach can distinguish News Story Tellers from News Propagators in the news agencies community as well as provide information that helps to understand the flow patterns between different news groups.