Pollution and the State: The Role of the Structure of Government

Government spending has significant environmental implications. This paper analyzes the effect of the allocation of government spending between public goods broadly defined and private goods or non-social subsidies on air and water pollution. The theoretical model predicts that a reallocation of expenditures from private subsidies to public goods improves environmental quality by reducing production pollution. We estimate an empirical model that shows that such a reallocation causes a significant reduction in air pollutants namely sulfur dioxide and lead and an improvement in water quality measures including dissolved oxygen and biological oxygen demand.

R-PEKS: RBAC Enabled PEKS for Secure Access of Cloud Data

In the recent past, few works have been done by combining attribute-based access control with multi-user PEKS, i.e., public key encryption with keyword search. Such attribute enabled searchable encryption is most suitable for applications where the changing of privileges is done once in a while. However, to date, no efficient and secure scheme is available in the literature that is suitable for these applications where changing privileges are done frequently. In this paper our contributions are twofold. Firstly, we propose a new PEKS scheme for string search, which, unlike the previous constructions, is free from bi-linear mapping and is efficient by 97% compared to PEKS for string search proposed by Ray et.al in TrustCom 2017. Secondly, we introduce role based access control (RBAC) to multi-user PEKS, where an arbitrary group of users can search and access the encrypted files depending upon roles. We termed this integrated scheme as R-PEKS. The efficiency of R-PEKS over the PEKS scheme is up to 90%. We provide formal security proofs for the different components of R-PEKS and validate these schemes using a commercial dataset

Emulation of microprocessor memory systems using the RAMP design framework

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 49-50).With the computer hardware industry and the academic world focused on multiprocessor systems, the RAMP project is aiming to provide the infrastructure for supporting high-speed emulation of large scale, massively-parallel multiprocessor systems using FPGAs. The RAMP design framework provides the platform for building this infrastructure. This research utilizes this design framework to emulate various microprocessor memory systems through a model built in an FPGA. We model both the latency and the bandwidth of memory systems through a parameterized emulation platform, thereby, demonstrating the validity of the design framework. We also show the efficiency of the framework through an evaluation of the utilized FPGA resources.by Asif I. Khan.S.M

Cycle-accurate modeling of multicore processors on FPGAs

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 169-176).We present a novel modeling methodology which enables the generation of a high-performance, cycle-accurate simulator from a cycle-level specification of the target design. We describe Arete, a full-system multicore processor simulator, developed using our modeling methodology. We provide details on Arete's resource-efficient and high-performance implementation on multiple FPGA platforms, and the architectural experiments performed using it. We present clear evidence that the use of simplified models in architectural studies can lead to wrong conclusions. Through two experiments performed using both cycle-accurate and simplified models, we show that on one hand there are substantial quantitative and qualitative differences in results, and on the other, the results match quite well.by Asif Imtiaz Khan.Ph.D

Financial Performance Comparison of Islamic and conventional banks in the United Arab Emirates (UAE)

This paper examines the financial performance of Islamic and commercial banks in the United Arab Emirates (UAE). The paper gives an empirical insights and comparisons between the performance of Islamic and conventional banking sectors.  The sample of the study consists of 5 fully-fledged Islamic banks and 14 conventional banks working in the UAE under the period 2011-2014. The study uses descriptive analysis, correlation, independent sample t test and multiple regression analysis to assess the performance and to compare between both types of banks. The Return on Assets (ROA) is used as proxy for profitability for both types of banks while bank size (log A), liquidity, capital adequacy, financial risk and operating efficiency as proxies for financial performance for both types of banks. The results showed that there is no significant difference between Islamic banks and conventional banks in terms of profitability (ROA) while there is a significant difference between Islamic and conventional banks in terms of liquidity, operation efficiency, capital adequacy, and financial risk. Further, the results indicated that the Islamic banks have higher operating efficiency, bank size and more liquidity than their counterparts of UAE. However, conventional banks are found to have better capital adequacy ratio than Islamic banks. In terms of financial risk, Islamic banks are found to have higher five times than conventional banks which may reflect challenges in the area of risk management in Islamic banks. Keywords: Financial performance, Islamic banks, Conventional banks, ROA, UAE. JEL Classification: A10, E60, G2

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

The recognition of activities of daily living (ADL) in smart environments is a well-known and an important research area, which presents the real-time state of humans in pervasive computing. The process of recognizing human activities generally involves deploying a set of obtrusive and unobtrusive sensors, pre-processing the raw data, and building classification models using machine learning (ML) algorithms. Integrating data from multiple sensors is a challenging task due to dynamic nature of data sources. This is further complicated due to semantic and syntactic differences in these data sources. These differences become even more complex if the data generated is imperfect, which ultimately has a direct impact on its usefulness in yielding an accurate classifier. In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning. This is achieved by identifying semantic correlations among sensor events through SPARQL queries, and by performing a time-series longitudinal imputation. Furthermore, we applied deep learning (DL) based artificial neural network (ANN) on public datasets to demonstrate the applicability and validity of the proposed approach. The results showed a higher accuracy with semantically imputed datasets using ANN. We also presented a detailed comparative analysis, comparing the results with the state-of-the-art from the literature. We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models

Human Gait Recognition Subject to Different Covariate Factors in a Multi-View Environment

Human gait recognition system identifies individuals based on their biometric traits. A human’s biometric features can be grouped into physiologic or behavioral traits. Biometric traits, such as the face [1], ears [2], iris [3], finger prints, passwords, and tokens, require highly accurate recognition and a well-controlled human interaction to be effective. In contrast, behavioral traits such as voice, signature, and gait do not require any human interaction and can be collected in a hidden and non-invasive mode with a camera system at a low resolution. In comparison with other physiological traits, one of the main advantages of gait analysis is the collection of data from a certain distance. However, gait is less powerful than physiological traits, yet it still has widespread application in surveillance for unfavorable situations. From traditional algorithms to deep learning models, a gait survey provides a detailed history of gait recognition