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

Development Of A User Friendly Liquid Level Measuring System.

The paper deals with the theory, design, fabrication and testing of a sensor and associated measuring system, which can be used for direct display of levels of conducting as well as non-conducting liquids

CSLM: Levenberg Marquardt based Back Propagation Algorithm Optimized with Cuckoo Search

Training an artificial neural network is an optimization task, since it is desired to find optimal weight sets for a neural network during training process. Traditional training algorithms such as back propagation have some drawbacks such as getting stuck in local minima and slow speed of convergence. This study combines the best features of two algorithms; i.e. Levenberg Marquardt back propagation (LMBP) and Cuckoo Search (CS) for improving the convergence speed of artificial neural networks (ANN) training. The proposed CSLM algorithm is trained on XOR and OR datasets. The experimental results show that the proposed CSLM algorithm has better performance than other similar hybrid variants used in this study

Green and sustainable construction practices impact on Organizational Development

Green Marketing plays important role in the organizational performance irrespective to the industry and the type of project. However green construction research lacks in the field of a construction project. The basic purpose of this work was to highlight the impact of modern techniques such as green construction on organizational performance through the adoption of sustainable practices in business strategies in the construction industry. Data were collected from 132 organizations, working on different construction projects located within the city of Rawalpindi and Islamabad, through an online questionnaire survey in two strata in terms of consultant and contractors. Data were analyzed through different tests, included Pearson’s correlation coefficient as well as regression using IBM SPSS Statistics Version 20. The study indicated that green construction has a strong correlation and positive impact on organizational performance, and this correlation partially mediated by sustainable development. The research findings have practical implications both in organizational and project manager’s perspectives. This research was limited to a specific geographic area due to time and cost constraints. Future researchers may opt to conduct the study in other geographic areas of Pakistan and in different industries. Moreover, additional or different mediating variables can also be used in future work

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

The development of Knowledge-Shelf to support the generation of a set-based design of Surface Jet Pump

Set-based Concurrent Engineering (SBCE) is advocated in order to provide an environment where design space is explored thoroughly leading to enhanced innovation. This is achieved by considering an alternative set of solutions after gaining knowledge to narrow down the solutions until the optimal solution is reached. Knowledge provision is essential in SBCE application. Hence there is a need for a tool that provides appropriate knowledge environment to enable SBCE and supports it in taking right decisions. At the same time there is a need to capture the rationale of the alternative design decisions taken during the process of narrowing down the set of the design in the SBCE environment. These decision rationales constitute important knowledge to be re-used in developing new products. In this research the tool designed to address this research rationale is called Knowledge-Shelf (K-Shelf). This paper and its outcome serve the groundwork for the development of K-Shelf software that captures knowledge and in generating the first design set in SBCE environment based on previous knowledge documented. This paper is a collaborative work from a case study of Surface Jet Pump (SJP) between the LeanPPD research group in Cranfield University and Caltec Ltd, a company that provides engineering solutions to the oil and gas industry. The K-Shelf was developed using rapid web application development tool - Oracle APE

The Impact of Green Human Resource Management on Green Recovery Performance: A Moderated Mediation Model

The main idea of this paper is to highlight the importance of environmental sustainability along with importance of implementation of green human resource management practices within an organization. Mediating role of pro environmental behaviors between the relationship of green human resource management and green recovery performance is the focus of study. It also looks at the moderating effect of ethical leadership style to achieve enhanced green recovery performance. The approach used for this study is quantitative and deductive. The researcher collected data through an electronic questionnaire and personally administration from 10 industries selected as per convince from PBD. Hayes process was used to analyze the mediating effect. Two hundred and fifty responses used as a sample for this study. The results confirm the findings of previous\researches conducted in other cultures. It also confirms that pro environmental behavior mediates the relationship of green human resource management and green recovery performance, and the Ethical leadership style (ELP) not moderate the relationship between GHRM and GRP and between GHRM and PEB respectively. This research provides guidelines for the researchers, policy makers, and managers