Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Cloud computing & Fuzzy logic. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Microbial Concrete: Way to Enhance the Durability of Building Structures

Abstract: Natural processes, such as weathering, faults, land subsidence, earthquakes, and human activities, create fractures and fissures in concrete structures that can reduce the service life of the structures. A novel strategy to restore or remediate such structures is biomineralization of calcium carbonate using microbes, such as those in the genus of the Bacillus species. The present study investigated the effects of Bacillus sp. CT-5, isolated from cement, on compressive strength and water-absorption tests. The results showed a 36% increase in compressive strength of cement mortar with the addition of bacterial cells. Treated cubes absorbed six times less water than control cubes as a result of microbial calcite deposition. The current work demonstrated that production of "microbial concrete" by Bacillus sp. on constructed facilities could enhance the durability of building materials.

Certificateless Searchable Public Key Encryption Scheme for Industrial Internet of Things

Abstract: With the widespread adoption of Internet of Things and cloud computing in different industry sectors, an increasing number of individuals or organizations are outsourcing their Industrial Internet of Things (IIoT) data in the cloud server to achieve cost saving and collaboration (e.g., data sharing). However, in this environment, preserving the privacy of data remains a key challenge and inhibiting factor to an even wider adoption of IIoT in the cloud environment. To mitigate these issues, in this paper, we design a new secure channel-free certificateless searchable public key encryption with multiple keywords scheme for IIoT deployment. We then demonstrate the security of the scheme in the random oracle model against two types of adversaries, where one adversary is given the power to choose a random public key instead of any user's public key and another adversary is allowed to learn the system master key. In the presence of these types of adversaries, we evaluate the performance of the proposed scheme and demonstrate that it achieves (computational) efficiency with low communication cost.

Mapping the Intellectual Structure of Social Entrepreneurship Research: A Citation/Co-citation Analysis

Abstract: In this paper, we employ bibliometric analysis to empirically analyse the research on social entrepreneurship published between 1996 and 2017. By employing methods of citation analysis, document co-citation analysis, and social network analysis, we analyse 1296 papers containing 74,237 cited references and uncover the structure, or intellectual base, of research on social entrepreneurship. We identify nine distinct clusters of social entrepreneurship research that depict the intellectual structure of the field. The results provide an overall perspective of the social entrepreneurship field, identifying its influential works and analysing scholarly communication between these works. The results further aid in clarifying the overall centrality features of the social entrepreneurship research network. We also examine the integration of ethics into social entrepreneurship literature. We conclude with a discussion on the structure and evolution of the social entrepreneurship field.

Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN: A Social Multimedia Perspective

Abstract: The continuous development and usage of multi-media-based applications and services have contributed to the exponential growth of social multimedia traffic. In this context, secure transmission of data plays a critical role in realizing all of the key requirements of social multimedia networks such as reliability, scalability, quality of information, and quality of service (QoS). Thus, a trust-based paradigm for multimedia analytics is highly desired to meet the increasing user requirements and deliver more timely and actionable insights. In this regard, software-defined networks (SDNs) play a vital role; however, several factors such as as-runtime security, and energy-aware networking limit its capabilities to facilitate efficient network control and management. Thus, with the view to enhance the reliability of the SDN, a hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in the context of social multimedia is proposed. It consists of the following two modules: 1) an anomaly detection module that leverages improved restricted Boltzmann machine and gradient descent-based support vector machine to detect the abnormal activities, and 2) an end-to-end data delivery module to satisfy strict QoS requirements of the SDN, that is, high bandwidth and low latency. Finally, the proposed scheme has been experimentally evaluated on both real-time and benchmark datasets to prove its effectiveness and efficiency in terms of anomaly detection and data delivery essential for social multimedia. Further, a large-scale analysis over a Carnegie Mellon University (CMU)-based insider threat dataset has been conducted to identify its performance in terms of detecting malicious events such as-Identity theft, profile cloning, confidential data collection, etc.