This comprehensive historical exploration investigates the intricate relationship between religion and the evolution of civil laws. Throughout human history, the interplay between religious beliefs and legal systems has profoundly shaped societies and governance structures. From ancient civilizations to the modern era, religious authorities and teachings have acted as catalysts in shaping civil laws, often with the goal of promoting social justice. This article embarks on a journey through time, unraveling the multifaceted connections between religion and the development of legal frameworks. (...) In numerous societies, religious doctrines formed the cornerstone of civil laws and regulations. The majority of civilizations witnessed priests assuming pivotal roles as lawmakers, effectively coupling civil laws and religious practices. By examining various historical civilizations, this study illustrates how religious norms and practices were intertwined with legal codes, revealing instances where religious teachings played a crucial role in promoting adherence to laws. The transition to modern times witnessed the emergence of alternative means for law enforcement, yet religion’s role persisted as an internal driving force for fostering law-abiding behavior. Drawing from examples such as the Code of Hammurabi, Islamic legal traditions, and the Roman Twelve Tables, this article showcases historical instances where religious values contributed to legal principles centered around equity, justice, and morality. Additionally, the study explores how religion’s influence shifted over time, from early religious leaders acting as legislators to the establishment of more secular legal systems. (shrink)

Visual cryptography is a cryptographic technique that allows visual information to be encrypted so that the human optical system can perform the decryption without any cryptographic computation. The halftone visual cryptography scheme (HVCS) is a type of visual cryptography (VC) that encodes the secret image into halftone images to produce secure and meaningful shares. However, the HVC scheme has many unsolved problems, such as pixel expansion, low contrast, cross-interference problem, and difficulty in managing share images. This article aims to enhance (...) the visual quality and avoid the problems of cross-interference and pixel expansion of the share images. It introduces a novel optimization of color halftone visual cryptography (OCHVC) scheme by using two proposed techniques: hash codebook and construction techniques. The new techniques distribute the information pixels of a secret image into a halftone cover image randomly based on a bat optimization algorithm. The results show that these techniques have enhanced security levels and make the proposed OCHVC scheme more robust against different attacks. The OCHVC scheme achieves mean squared error (MSE) of 95.0%, peak signal-to-noise ratio (PSNR) of 28.3%, normalized cross correlation (NCC) of 99.4%, and universal quality index (UQI) of 99.3% on average for the six shares. Subsequently, the experiment results based on image quality metrics show improvement in size, visual quality, and security for retrieved secret images and meaningful share images of the OCHVC scheme. Comparing the proposed OCHVC with some related works shows that the OCHVC scheme is more effective and secure. (shrink)

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT (...) systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%. (shrink)

Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence (...) of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system. (shrink)

al-juzʼ 1. [Without special title] -- al-juzʼ 2. al-Masmūḥ wa-al-mamnūʻ fī firāsh al-zawjīyah.

Ten years after the popular uprising that became a brutal war, Syrian and Syrian-Palestinian authors are engaged in the struggle to craft a historical consciousness that can acknowledge and mourn for their recent revolutionary past without reifying it. As they write in and of material, political, and social ruin, their works echo collective traumas in regional memory: the Palestinian nakba, the rise of Syria’s Assad regime, Lebanon’s civil war, the 2003 occupation of Iraq, and more. The ruin appears cruelly recursive, (...) yet it is arrested in the corpus of works discussed in this article: poetry by Firas Sulaiman and Osama Esber; prose by Yassin al-Haj Saleh, Samer Yazbek, and Ra’id Wahsh; and documentary film by the Abounaddara Collective. Drawing on Arabic poetic modernism and regional politics, I argue that ruin imagery—ranging from war’s rubble to ancient artefacts—carries distinctive structures of temporal anticipation in Syrian literary and cultural memory. The writers and filmmakers discussed deploy formal and thematic means of stasis and repetition, displacement and accumulation, to summon these temporal structures—only to refuse, interrupt, and reroute them. I argue that such poetic engineerings of the images of ruin assert the singularity of the Syrian present within broader collective memories of ruin. As such, they raise a historicizing challenge to the current academic dominance of reading ruin imagery, notably from the Middle East, through an imperial lens. (shrink)