Corporate Governance for Banks in Pakistan: Recent Developments and Regional Comparisons

The emerging economies in the South Asian region have embarked on a bold reform process to develop the banking sector. This development has improved the transparency and accountability of the banking sector because these countries focused on ‘best practice’ corporate governance for banks. In view of a rapidly developing market with a slow pace of information dissemination, adverse selection and moral hazard problems are likely to be on the rise and may need a mechanism to train and discipline bank management. It was, therefore timely for the central banks in the region to introduce a ‘best practice’ for the banking system as a whole. This study provides a survey of recent developments in corporate governance of the banking sector in Pakistan and a comparison of similar developments in two other regional economies, namely, India and Bangladesh. In addition to a theoretical discussion on this issue, we also provide an overview of the banking sector restructuring and highlighting important features of the codes of corporate governance established by central banks in the sample countries. In conclusion, we present a comparison of the major differences in these measures across countries and comment on the pace of these developments.Corporate Governance, Banks, Pakistan

Corporate Governance for Banks in Pakistan : Recent Developments and Regional Comparisons

The emerging economies in the South Asian region have embarked on a bold reform process to develop the banking sector. This development has improved the transparency and accountability of the banking sector because these countries focused on best practice corporate governance for banks. In view of a rapidly developing market with a slow pace of information dissemination, adverse selection and moral hazard problems are likely to be on the rise and may need a mechanism to train and discipline bank management. It was, therefore timely for the central banks in the region to introduce a best practice for the banking system as a whole. This study provides a survey of recent developments in corporate governance of the banking sector in Pakistan and a comparison of similar developments in two other regional economies, namely, India and Bangladesh. In addition to a theoretical discussion on this issue, we also provide an overview of the banking sector restructuring and highlighting important features of the codes of corporate governance established by central banks in the sample countries. In conclusion, we present a comparison of the major differences in these measures across countries and comment on the pace of these developments.Banking, Corporate governance, banking sector restructuring

Association Between Placenta Previa and Preeclampsia

Background: To determine association between placenta previa and pre-eclampsia in pregnant women presenting to a tertiary care hospital.Methods: In this prospective study 187 pregnant women with placenta previa and 187 pregnant, total 374, women without placenta previa were enrolled. Ultrasonography examination was performed on all patients to ascertain the attachment of placenta on uterine wall. All patients were followed every fourth week till 38th weeks. Pre-eclampsia was labelled if mean of three readings of blood pressure was more than 139/89 in a pregnant woman with history of normal blood pressure before pregnancy and proteinuria on urine laboratory examination.Results: Mean age was 27.23 ± 3.633 and ranged from 21 to 43 years. Primipara were 45.7% and 54.3% were multipara. Eight patients (2.1%) were having pre-eclampsia. All patients belonged to non- placenta previa group. Relative risk came out 1.045 ranging from1.014 to 1.077 at 95% confidence interval. There was no effect of age and parity on the association.Conclusion: There is a protective association between placenta previa and pre-eclampsia in pregnant women

Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks

Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs). Recent works have shown that DNNs are vulnerable to adversarial attacks, i.e., small perturbations added to the input data can lead to targeted or random misclassifications. In this paper, we aim at investigating the key research question: ``Are SNNs secure?'' Towards this, we perform a comparative study of the security vulnerabilities in SNNs and DNNs w.r.t. the adversarial noise. Afterwards, we propose a novel black-box attack methodology, i.e., without the knowledge of the internal structure of the SNN, which employs a greedy heuristic to automatically generate imperceptible and robust adversarial examples (i.e., attack images) for the given SNN. We perform an in-depth evaluation for a Spiking Deep Belief Network (SDBN) and a DNN having the same number of layers and neurons (to obtain a fair comparison), in order to study the efficiency of our methodology and to understand the differences between SNNs and DNNs w.r.t. the adversarial examples. Our work opens new avenues of research towards the robustness of the SNNs, considering their similarities to the human brain's functionality.Comment: Accepted for publication at the 2020 International Joint Conference on Neural Networks (IJCNN

QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks

Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs). In this paper, we propose two quantization-based defense mechanisms, Constant Quantization (CQ) and Trainable Quantization (TQ), to increase the robustness of CNNs against adversarial examples. CQ quantizes input pixel intensities based on a "fixed" number of quantization levels, while in TQ, the quantization levels are "iteratively learned during the training phase", thereby providing a stronger defense mechanism. We apply the proposed techniques on undefended CNNs against different state-of-the-art adversarial attacks from the open-source \textit{Cleverhans} library. The experimental results demonstrate 50%-96% and 10%-50% increase in the classification accuracy of the perturbed images generated from the MNIST and the CIFAR-10 datasets, respectively, on commonly used CNN (Conv2D(64, 8x8) - Conv2D(128, 6x6) - Conv2D(128, 5x5) - Dense(10) - Softmax()) available in \textit{Cleverhans} library

Antecedents of self-disclosure on social networking sites (SNSs): A study of Facebook users

Self-disclosure on social networking sites (SNSs) leads to social capital development, connectedness, and relationship building. Due to several benefits associated with this behavior, self-disclosure has become a subject of research over the last few years. The current study investigates the antecedents of self-disclosure under the lens of the technology acceptance model (TAM). The research is quantitative, and the data were collected from 400 Pakistani Facebook users with a variety of demographic characteristics. The partial least squares-structural equation model (PLS-SEM) analysis technique was employed to analyze the data. The study′s findings confirmed that perceived usefulness is a strong predictor of personal information sharing, and it along with other variables causes a 31% variation in self-disclosure behavior. However, trust (medium and social) mediates the relationship of perceived usefulness, privacy concerns, and self-disclosure behavior

Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference

The exponential increase in dependencies between the cyber and physical world leads to an enormous amount of data which must be efficiently processed and stored. Therefore, computing paradigms are evolving towards machine learning (ML)-based systems because of their ability to efficiently and accurately process the enormous amount of data. Although ML-based solutions address the efficient computing requirements of big data, they introduce (new) security vulnerabilities into the systems, which cannot be addressed by traditional monitoring-based security measures. Therefore, this paper first presents a brief overview of various security threats in machine learning, their respective threat models and associated research challenges to develop robust security measures. To illustrate the security vulnerabilities of ML during training, inferencing and hardware implementation, we demonstrate some key security threats on ML using LeNet and VGGNet for MNIST and German Traffic Sign Recognition Benchmarks (GTSRB), respectively. Moreover, based on the security analysis of ML-training, we also propose an attack that has a very less impact on the inference accuracy. Towards the end, we highlight the associated research challenges in developing security measures and provide a brief overview of the techniques used to mitigate such security threats

Maternal Mortality in Rural Areas of Pakistan: Challenges and Prospects

Pakistan is one of the countries in South Asia ranking high in maternal mortality rate. Though, a signatory of Agenda 2030, the country still lags behind considerably in achieving Sustainable Development Goals (SDGs). The ratio of maternal mortality is, even higher in rural areas of the country. Lack of health care facilities, education, malnutrition, poverty, high prevalence of violence against women in rural areas, and socioeconomic factors are some of the major contributing elements for elevated levels of maternal mortality and morbidity rate in Pakistan. By making inclusive policies at the national level to improve the reach of the rural population to healthcare facilities, educating women and eliminating gender-based disparities, introducing family planning interventions, accountability, and continuity of democracy are essentially needed to improve maternal health in Pakistan’s rural areas. This chapter focuses on challenges to maternal health in rural areas and possible options to resolve these issues

Dissecting the genomics structure of proteus mirabilis strain PR03 using whole genome sequencing approach / Mohd Ikhmal Hanif Abdul Khalid

Study background: Proteus mirabilis is a common Gram-negative bacterium which causes upper urinary tract infection and re-current infection. With cutting-edge technology such as whole genome sequencing, the genome sequence could be fully explored to understand its pathogenic and virulence genes. This study aims to provide better understanding on its mechanisms to invade, infect, colonize host epithelial cells and evade host immune system. Method: DNA of local clinical isolate of Proteus mirabilis strain PR03 was extracted and subjected to whole genome sequencing using the Illumina second generation sequencer, Genome Analyzer II (Illumina, California, USA). The genomic data was trimmed, analyzed, assembled and annotated using bioinformatics pipeline to identify genes that contribute to the pathogenicity and virulence of the strain. The genome was compared with P. mirabilis strain HI4320 to identify genes of similarities and differences. Results: The genome size of P. mirabilis strain PR03 is 3.9 Mbp with a G+C content of 38.6%. This strain has 3 465 genes and 53 RNA. Flagella, fimbriae, capsule, cell membrane, cell wall, urease, invasion proteins and stress respond genes were identified that contribute to the pathogenic and virulence factors of this strain. Genomes comparison showed this species has 56.25% of essential genes, 39.25% of dispensable genes and 4.47% of strain specific genes. Conclusion: P. mirabilis strain PR03 was successfully sequenced, assembled and annotated. 23.39% of P. mirabilis strain PR03 total genes were identified to contribute it pathogenicity and virulence. The genome sequences were successfully deposited in NCBI genomic database