XML is one of the most important standards for manipulating data on the Internet. However, querying large volumes of XML data represents a bottleneck for several computationally intensive applications. A solution is to pre-process the document in streaming mode with resources approximately proportional to document depth and query selectivity. Limited processing space can then accommodate much larger documents. But the actual savings vary so much as to make them unpredictable. To overcome this limitation of stream-processing we propose a new application of the path tree synopsis data structure. Such a synopsis provides a succinct description of the original document with low computational overhead and high accuracy for processing tasks like selectivity estimation and query answer approximation. In this paper, we formally define the path tree synopsis, informally introduced by and used by, and propose a new streaming algorithm to construct it. We also present an online stream-querying system able to estimate the cost for a given query before answering it accurately. The core algorithm is adapted from LQ, we apply it to path tree traversal, cost estimation, query processing and even optimizations.

Electricity and water are vital resources, but the current management systems face challenges due to growing demand and regulatory complexities. To address this, we introduce a novel blockchain-based solution for managing electricity and water services. Our proposed system connects various entities through smart contracts on the blockchain, automates processes, and ensures transparency. Customers can easily view and pay bills, track consumption, and enjoy secure online transactions while preserving their privacy. The shared digital ledger enhances trust among entities and promotes transparency. Additionally, our system contributes to environmental sustainability by reducing paper usage, incentivizing energy-saving devices, and efficiently managing electricity and water consumption. Finally, a meta-analysis of the related work is conducted to highlight the importance of our solution.

Malware detection is a challenging and non-trivial task due to ever increase in several attacks and their sophistication level. Detection of such attacks demands the exploration of new approaches to generalize the attack patterns. One such approach is the use of Monte-Carlo simulations to train a reinforcement learning model. In this paper, we propose a self-adaptive Monte-Carlo simulation-based reinforcement model called Heuristic-based Generative Model (HGM), which generalizes the attack patterns in such a way that the new unknown attacks can be detected and flagged in real-time. The results show that HGM can detect a variety of malware with high accuracy.

Android has become the dominant operating system for portable devices, making it a valuable asset that needs protection. Though Android is very popular; it has several vulnerabilities which attackers use for malicious intents. In this paper, we present a comprehensive study on the threats in Android OS that various malware developers exploit and the different malware functionality based on Android’s threats. Furthermore, we analyze and evaluate the anti-malware approaches implemented to face the malware functionalities. Finally, we analyze and categorize malware developers’ most common anti-analysis techniques to evade anti-malware approaches. It comes to our attention that many papers covered each topic separately; however, we could not find one comprehensive study that covers Android with such details that it could be used as a research handbook on Android malware. This is the main novelty and contribution of this work.