What is liberal Islam? The elusive reformation

While Islam as traditionally understood may not favor liberalism, the more "liberal" interpretations of Islam are not democracy friendly either, mainly because they lack wide popular support. The search for an "elusive" Islamic Reformation may not thus be right way to approach the democracy question. Even if such a "Reformation" were to materialize, it is likely to be divisive and disruptive in the short and medium terms. Meanwhile, the liberal movements with the most promising democratic potential appear to be those which have bypassed the theological question altogether and worked to build broad pro-democracy coalitions by agreeing to bypass divisive issues politicians are not equipped to solve

Optimizing cybersecurity incident response decisions using deep reinforcement learning

The main purpose of this paper is to explore and investigate the role of deep reinforcement learning (DRL) in optimizing the post-alert incident response process in security incident and event management (SIEM) systems. Although machine learning is used at multiple levels of SIEM systems, the last mile decision process is often ignored. Few papers reported efforts regarding the use of DRL to improve the post-alert decision and incident response processes. All the reported efforts applied only shallow (traditional) machine learning approaches to solve the problem. This paper explores the possibility of solving the problem using DRL approaches. The main attraction of DRL models is their ability to make accurate decisions based on live streams of data without the need for prior training, and they proved to be very successful in other fields of applications. Using standard datasets, a number of experiments have been conducted using different DRL configurations The results showed that DRL models can provide highly accurate decisions without the need for prior training

Studying my movement: social science without cynicism

بعد أن فرغت من إنجاز دراستي للدكتوراه في نهاية عام 1989، التحقت بكلية سانت أنطوني في جامعة أوكسفورد كزميل باحث، حيث عكفت على إعداد الرسالة للنشر، وكذلك على إعداد ونشر كتابي "من يحتاج الدولة الإسلامية" (صدر كلاهما في عام 1991). وفي نفس الأثناء اشتغلت بإعداد ورقتين للنشر، الأولى بعنوان: "الطريق الطويل من لاهور إلى الخرطوم: أبعد من الإصلاح الإسلامي"، التي نشرت في مجلة الجمعية البريطانية لدراسات الشرق الأوسط (1990)، ودراسة أخرى بعنوان: "دراسة حركتي: العلوم الاجتماعية غير منزوعة الأخلاق". كانت هذه الدراسة عبارة عن تأملات منهجية حول إعداد دراسة عن حركة إسلامية من الداخل، مع مراعاة المعايير المتبعة في الجامعات الغربية. بعثت بتلك الورقة إلى المجلة الدولية لدراسات الشرق الأوسط، المعروفة اختصارًا بـIJMES، وكانت تلك تجربتي الأولى في التعامل مع الدوريات العلمية العالمية. ولم أكن أعرف وقتها أن هذه الدورية كانت في المقدمة في مجالها، بحيث تضع شروطًا غاية في الصرامة لمن يتقدم للنشر فيها. ومضت عدة شهور قبل أن تصلني رسالة من إدارة التحرير في الدورية، وكانت من رئيسة التحرير شخصيًّا، وهو أمر نادرًا ما يحدث كما علمت فيما بعد. وكان الأدعى للعجب من محتوى الرسالة ملحقاتها. فقد أرفقت رئيسة التحرير الدكتورة ليلى فواز برسالتها، تقارير من ستة محكمين؛ اثنان منهم رفضوا نشر الورقة، واثنان رأيا أنها قد تصلح لمنبر آخر، بينما وافق اثنان فقط على نشرها. وقد أبلغتني بأن معظم المحكمين كما ترى رفضوا نشرها، ولكنها قررت مع ذلك أن تنشرها. وطلبت مني أن أرد على ما ورد من انتقادات وتعليقات، إما بتعديل ما كتبت، أو بتعليل الرفض. وبالفعل قمت بذلك، ونشرت الورقة في عام 199

General queueing network models for computer system performance analysis. A maximum entropy method of analysis and aggregation of general queueing network models with application to computer systems.

In this study the maximum entropy formalism [JAYN 57] is suggested as an alternative theory for general queueing systems of computer performance analysis. The motivation is to overcome some of the problems arising in this field and to extend the scope of the results derived in the context of Markovian queueing theory. For the M/G/l model a unique maximum entropy solution., satisfying locALl balance is derived independent of any assumptions about the service time distribution. However, it is shown that this solution is identical to the steady state solution of the underlying Marko-v process when the service time distribution is of the generalised exponential (CE) type. (The GE-type distribution is a mixture of an exponential term and a unit impulse function at the origin). For the G/M/1 the maximum entropy solution is identical in form to that of the underlying Markov process, but a GE-type distribution still produces the maximum overall similar distributions. For the GIG11 model there are three main achievements: first, the spectral methods are extended to give exaft formulae for the average number of customers in the system for any G/G/l with rational Laplace transform. Previously, these results are obtainable only through simulation and approximation methods. (ii) secondly, a maximum entropy model is developed and used to obtain unique solutions for some types of the G/G/l. It is also discussed how these solutions can be related to the corresponding stochastic processes. (iii) the importance of the G/GE/l and the GE/GE/l for the analysis of general networks is discussed and some flow processes for these systems are characterised. For general queueing networks it is shown that the maximum entropy solution is a product of the maximum entropy solutions of the individual nodes. Accordingly, existing computational algorithms are extended to cover general networks with FCFS disciplines. Some implementations are suggested and a flow algorithm is derived. Finally, these results are iised to improve existing aggregation methods. In addition, the study includes a number of examples, comparisons, surveys, useful comments and conclusions

A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis

Over the past few years, much work has been done to develop machine learning models that perform Arabic sentiment analysis (ASA) tasks at various levels and in different domains. However, most of this work has been based on shallow machine learning, with little attention given to deep learning approaches. Furthermore, the deep learning models used for ASA have been based on noncontextualized embedding schemes that negatively impact model performances. This article proposes a novel deep learning-based multilevel parallel attention neural (MPAN) model that uses a simple positioning binary embedding scheme (PBES) to simultaneously compute contextualized embeddings at the character, word, and sentence levels. The MPAN model then computes multilevel attention vectors and concatenates them at the output level to produce competitive accuracies. Specifically, the MPAN model produces state-of-the-art results that outperform all established ASA baselines using 34 publicly available ASA datasets. The proposed model is further shown to produce new state-of-the-art accuracies for two multidomain collections: 95.61% for a binary classification collection and 94.25% for a tertiary classification collection. Finally, the performance of the MPAN model is further validated using the public IMDB movie review dataset, on which it produces an accuracy of 96.13%, placing it in second position on the global IMDB leaderboard

An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.publishedVersio

Topical co-delivery of indomethacin and nigella sativa L. essential oil in poly-cappa-caprolactone nanoparticles: in vitro study of anti-inflammatory activity

Indomethacin is a potent, nonselective Non-steroidal Antiinflammatory Drug (NSAID) but its low water-solubility precludes its use as topical dosage form. As with other NSAIDs, the systemic delivery is associated with high risk of serious gastrointestinal adverse events including bleeding, ulceration and perforation of stomach and intestines. Here we demonstrate a safer way of administration i.e via topical demonstrating synergistic effects when co-delivered with Nigella sativa L. seeds essential oil (NSSEO) in the form of coencapsulated particles (~200 nm) of poly--caprolactone. The particles showed penetrability across stratum corneum to dermis layer in ex-vivo human skin. Further study in the xyline-induced ear edema in mice was performed, and co-encapsulated particles demonstrated highest antiinflammatory effect compared to indomethacin particles and indomethacin gels. Despite slower onset compared to indomethacin gels, the inflamed ear continued to show reduction in thickness over 8 hours of observation demonstrating synergistic and pro-longed effect contributed by NSSEO. In immunohistochemistry study of CD45+, the mice ears treated with co-encapsulated particles showed considerable reduction in lesions, epidermal-dermal separation and inflammatory cells (lymphocytes and neutrophils) infiltration as compared to other formulation. Based on microscopic evaluation, the anti-inflammatory inhibition effect of co-encapsulated particles is the highest (90%) followed by indomethacin particles (79%) and indomethacin gel (49%). The findings suggest not only skin permeability of indomethacin significantly improved but also the therapeutic effects, all provided by the presence of NSSEO in the particles. This study paves the way to more co-encapsulation of any other contemporary medicines in combination with this wholesome natural oil, NSSEO

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model

Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning

Social media networks have grown exponentially over the last two decades, providing the opportunity for users of the internet to communicate and exchange ideas on a variety of topics. The outcome is that opinion mining plays a crucial role in analyzing user opinions and applying these to guide choices, making it one of the most popular areas of research in the field of natural language processing. Despite the fact that several languages, including English, have been the subjects of several studies, not much has been conducted in the area of the Arabic language. The morphological complexities and various dialects of the language make semantic analysis particularly challenging. Moreover, the lack of accurate pre-processing tools and limited resources are constraining factors. This novel study was motivated by the accomplishments of deep learning algorithms and word embeddings in the field of English sentiment analysis. Extensive experiments were conducted based on supervised machine learning in which word embeddings were exploited to determine the sentiment of Arabic reviews. Three deep learning algorithms, convolutional neural networks (CNNs), long short-term memory (LSTM), and a hybrid CNN-LSTM, were introduced. The models used features learned by word embeddings such as Word2Vec and fastText rather than hand-crafted features. The models were tested using two benchmark Arabic datasets: Hotel Arabic Reviews Dataset (HARD) for hotel reviews and Large-Scale Arabic Book Reviews (LARB) for book reviews, with different setups. Comparative experiments utilized the three models with two-word embeddings and different setups of the datasets. The main novelty of this study is to explore the effectiveness of using various word embeddings and different setups of benchmark datasets relating to balance, imbalance, and binary and multi-classification aspects. Findings showed that the best results were obtained in most cases when applying the fastText word embedding using the HARD 2-imbalance dataset for all three proposed models: CNN, LSTM, and CNN-LSTM. Further, the proposed CNN model outperformed the LSTM and CNN-LSTM models for the benchmark HARD dataset by achieving 94.69%, 94.63%, and 94.54% accuracy with fastText, respectively. Although the worst results were obtained for the LABR 3-imbalance dataset using both Word2Vec and FastText, they still outperformed other researchers’ state-of-the-art outcomes applying the same dataset