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Graph neural networks have triggered a resurgence of graph-based text classification methods, defining today's state of the art. We show that a simple multi-layer perceptron (MLP) using a Bag of Words (BoW) outperforms the recent graph-based models TextGCN and Hete-GCN in an inductive text classification setting and is comparable with HyperGAT in singlelabel classification. We also run our own experiments on multi-label classification, where the simple MLP outperforms the recent sequentialbased gMLP and aMLP models. Moreover, we fine-tune a sequence-based BERT and a lightweight DistilBERT model, which both outperform all models on both single-label and multi-label settings in most datasets. These results question the importance of synthetic graphs used in modern text classifiers. In terms of parameters, DistilBERT is still twice as large as our BoW-based wide MLP, while graph-based models like TextGCN require setting up an O(N 2 ) graph, where N is the vocabulary plus corpus size.

Abstract. Browser Extensions (BE) enhance the core functionality of the Browser and provide customization to it. Browser extensions enjoy high privileges, sometimes with the same privileges as Browser itself. As a consequence, a vulnerable or malicious extension might expose Browser and system resources to attacks. This may put Browser resources at risk of unwanted operations, privilege escalation etc. BE can snoop on web applications, launch arbitrary processes, and even access files from host file system. In addition to that, an extension can even collude with other installed extensions to share objects and change preferences. Although well-intentioned, extension developers are often not security experts. Hence, they might end up writing vulnerable code. In this paper we present a new attacks via Browser extensions. In particular, the attack allows two malicious extensions to communicate and collaborate with each other in such a way to achieve a malicious goal. We identify the vulnerable points in extension development framework as: (a) object reference sharing, and (b) preference overriding. We illustrate the effectiveness of the proposed attack using various attack scenarios. Furthermore, we provide a proof-of-concept illustration for web domains including Banking & shopping. We believe that the scenarios we use in use-case demonstration underlines the severity of the presented attack. Finally, we also contribute an initial framework to address the presented attack.

No abstract

The immune system has the ability to provoke inflammation in response to a wide variety of different triggers. Toxic chemicals, infectious diseases, radiation, and cells that have been harmed are some examples of these stimuli. It removes the detrimental stimuli and at the same time initiates the healing process, which is a win-win situation. As a result, the protective reaction of inflammation is essential for ensuring that the body continues to function properly. The majority of the time, cellular and molecular activities and interactions work together to successfully minimise the risk of experiencing damage or infection during acute inflammatory reactions. This is because these activities and interactions are coordinated to function together. This review article was prepared utilising materials written in English, and it has been published in time intervals of 15 years beginning in 1995 and continuing all the way up until the current day. Both systematic reviews and randomised controlled trials (RCTs), which are considered to be the two most reliable types of research, were included in the collection of publications that were pertinent to the goal that we set for ourselves. The first two approaches are the only ones that should be prioritised above the others. Studies with an open label and studies with cohorts are not as essential as those with a case-control design, which are called preclinical trials.