Political Instability and Lessons for Pakistan: Case Study of 2014 PTI Sit in/Protests

Abstract. It’s a short allegory to present the case for the importance of Political stability in the economic progress of a country. The Arab spring protests were seen as strengthening democracy in the Arab world. Notwithstanding the surprise Arab spring brought in shape of further destabilizing Middle East, a similar environment of unrest and protests in a practicing democracy like Pakistan capture same dynamics of uncertainty that dampen economic destabilization. The paper briefly covers PTI’s sit in protests in year 2014 to make a case for how political instability stifled economic progress in Pakistan though momentarily.Keywords. Political economy, Pakistan economy.JEL. D72, F59, P16

Political Instability and Lessons for Pakistan: Case Study of 2014 PTI Sit in Protests

It’s a short allegory to present the case for the importance of Political stability in the economic progress of a country. The Arab spring protests were seen as strengthening democracy in the Arab world. Notwithstanding the surprise Arab spring brought in shape of further destabilizing Middle East, a similar environment of unrest and protests in a practicing democracy like Pakistan capture same dynamics of uncertainty that dampen economic destabilization. The paper briefly covers PTI’s sit in protests in year 2014 to make a case for how political instability stifled economic progress in Pakistan though momentarily

Political Instability and Lessons for Pakistan: Case Study of 2014 PTI Sit in Protests

It’s a short allegory to present the case for the importance of Political stability in the economic progress of a country. The Arab spring protests were seen as strengthening democracy in the Arab world. Notwithstanding the surprise Arab spring brought in shape of further destabilizing Middle East, a similar environment of unrest and protests in a practicing democracy like Pakistan capture same dynamics of uncertainty that dampen economic destabilization. The paper briefly covers PTI’s sit in protests in year 2014 to make a case for how political instability stifled economic progress in Pakistan though momentarily

Comparison of decompression alone versus decompression with fusion for stenotic lumbar spine: A systematic review and meta-analysis

The first line of treatment for lumbar spinal stenosis (with or without lumbar degenerative spondylolisthesis) involves conservative options such as anti-inflammatory drugs and analgesics. Approximately, 10%-15% of patients require surgery. Surgical treatment aims to decompress the spinal canal and dural sac from degenerative bony and ligamentous overgrowth. Different studies have given conflicting results. The aim of our study is to clear the confusion by comparing two surgical techniques. This meta-analysis was conducted in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was conducted of the Ovid Embase, Scopus, Pubmed, Ovid Medline, Google Scholar, and Cochrane library databases. A quality and risk of bias assessment was also done. The analysis was done using Revman software (The Nordic Cochrane Centre, The Cochrane Collaboration, 2014, Copenhagen, Denmark). A total of 76 studies were extracted from the literature search and 29 studies with relevant information were shortlisted. Nine studies were included in the meta-analysis after a quality assessment and eligibility. Fusion with decompression surgery was found to be a better technique when compared to decompression alone for spinal stenosis in terms of the Oswestry Disability index and the visual analog pain scale for back and leg pain. On the basis of the meta-analysis of the recent medical literature, the authors concluded that decompression with fusion is a 3.5-times better surgical technique than decompression alone for spinal stenosis

EDL-Det: A Robust TTS Synthesis Detector Using VGG19-Based YAMNet and Ensemble Learning Block

Various audio deep fake synthesis algorithms exist, such as deep voice, tacotron, fastspeech, and imitation techniques. Despite the existence of various spoofing speech detectors, they are not ready to distinguish unseen audio samples with high precision. In this study, we suggest a robust model, namely an Ensemble Deep Learning Detector (EDL-Det), to detect text-to-speech (TTS) and categorize it into spoofed and bonafide classes. Our proposed model is an improved method based on Yet Another Multi-scale Convolutional Neural Network (YAMNet) employing VGG19 as a base network combined with two other deep learning(DL) techniques. Our proposed system effectively analyzes the audio to extract better artifacts. We have added an ensemble learning block that consists of ResNet50 and InceptionNetv2. First, we convert speech into mel-spectrograms that consist of time-frequency representations. Second, we train our model using the ASVspoof-2019 dataset. Ultimately, we classified the audios, transforming them into mel-spectrograms using our trained binary classifier and a majority voting scheme by three networks. Due to ensemble architecture, our proposed model effectively extracts the most representative features from the mel-spectrograms. Furthermore, we have performed extensive experiments to assess the performance of the suggested model using the ASVspoof 2019 corpus. Additionally, our proposed model is robust enough to identify the unseen spoofed audios and accurately classify the attacks based on cloning algorithms

A Robust Framework for Object Detection in a Traffic Surveillance System

Object recognition is the technique of specifying the location of various objects in images or videos. There exist numerous algorithms for the recognition of objects such as R-CNN, Fast R-CNN, Faster R-CNN, HOG, R-FCN, SSD, SSP-net, SVM, CNN, YOLO, etc., based on the techniques of machine learning and deep learning. Although these models have been employed for various types of object detection applications, however, tiny object detection faces the challenge of low precision. It is essential to develop a lightweight and robust model for object detection that can detect tiny objects with high precision. In this study, we suggest an enhanced YOLOv2 (You Only Look Once version 2) algorithm for object detection, i.e., vehicle detection and recognition in surveillance videos. We modified the base network of the YOLOv2 by reducing the number of parameters and replacing it with DenseNet. We employed the DenseNet-201 technique for feature extraction in our improved model that extracts the most representative features from the images. Moreover, our proposed model is more compact due to the dense architecture of the base network. We utilized DenseNet-201 as a base network due to the direct connection among all layers, which helps to extract a valuable information from the very first layer and pass it to the final layer. The dataset gathered from the Kaggle and KITTI was used for the training of the proposed model, and we cross-validated the performance using MS COCO and Pascal VOC datasets. To assess the efficacy of the proposed model, we utilized extensive experimentation, which demonstrates that our algorithm beats existing vehicle detection approaches, with an average precision of 97.51%

A Robust Framework for Object Detection in a Traffic Surveillance System

Object recognition is the technique of specifying the location of various objects in images or videos. There exist numerous algorithms for the recognition of objects such as R-CNN, Fast R-CNN, Faster R-CNN, HOG, R-FCN, SSD, SSP-net, SVM, CNN, YOLO, etc., based on the techniques of machine learning and deep learning. Although these models have been employed for various types of object detection applications, however, tiny object detection faces the challenge of low precision. It is essential to develop a lightweight and robust model for object detection that can detect tiny objects with high precision. In this study, we suggest an enhanced YOLOv2 (You Only Look Once version 2) algorithm for object detection, i.e., vehicle detection and recognition in surveillance videos. We modified the base network of the YOLOv2 by reducing the number of parameters and replacing it with DenseNet. We employed the DenseNet-201 technique for feature extraction in our improved model that extracts the most representative features from the images. Moreover, our proposed model is more compact due to the dense architecture of the base network. We utilized DenseNet-201 as a base network due to the direct connection among all layers, which helps to extract a valuable information from the very first layer and pass it to the final layer. The dataset gathered from the Kaggle and KITTI was used for the training of the proposed model, and we cross-validated the performance using MS COCO and Pascal VOC datasets. To assess the efficacy of the proposed model, we utilized extensive experimentation, which demonstrates that our algorithm beats existing vehicle detection approaches, with an average precision of 97.51%