Invited Speakers (ICECC 2021)


Assoc. Prof. Rohaya Latip
University Putra Malaysia, Malaysia

Assoc. Prof. Dr. Rohaya Latip is an Associate Professor at Faculty of Computer Science and Information Technology, University Putra Malaysia. She holds a Ph. D in Distributed Database and Msc. in Distributed System from University Putra Malaysia. She graduated her Bachelor of Computer Science from University Technology Malaysia, Malaysia in 1999.

She is currently the head of Department of Communication Technology and Network. Her research interests include Big Data, Cloud and Grid Computing, Network management, and Distributed database. She served as an Associate Professor at Najran university, Kingdom of Arab Saudi (2012-2013). She is the Head of HPC section in University Putra Malaysia (2011-2012) and consulted the Campus Grid project and also the Wireless for hostel in Campus UPM project. She was also a Co-researcher at Institute for Mathematic Research (INSPEM) from 2011 to 2019. She is the editorial board of International Journal of Computer Networks and Communications Security (IJCNCS), editorial board of International Journal of Digital Contents and Applications (IJDCA) and editorial board for International Journal of Computer Networks and Applications (IJCNA).  

Speech Title: Optimization on Replication Performance via Balance Quorum (BQ) and Data Center Selection Method (DCSM) Algorithms in Cloud Environment
Abstract: The cloud replication environment is an established and prominent technology globally recognized to deal with the issue of high-volume data that users are expected to access from anywhere at any time. Abundant researchers embarked their efforts to develop heterogeneous strategies to complement the ambiguities of cloud platform and system requirements. Regardless the resilient and durable service technologies provided by cloud providers, the limitation in respective cloud replication strategies are inevitable. The key issue is when acquired data demanded to be always accessible to users irrespective of time and location, appears to be crucial problem which is relative to, many inefficient system implementations. Therefore, we selected existing research work named “Dynamic Popularity aware Replication Strategy (DPRS)” and we concentrated on finding the research strength, gaps and limitations. A thorough case study was conducted via establishing re-simulation on DPRS algorithm and rigorous review was focused on the algorithm process flow. Subsequently, this study specifically reveals the limitations in particular algorithm and proposed potential area for improvements. The case study was simulated using simulation tools called, CloudSim. Finally, in order to enhance multi-performance in cloud replication environments, algorithms are presented as part of the proposed model which would explicitly contributes betterments in storage consumptions and network usage.

Assoc. Prof. Indrarini D. Irawati
Telkom University, Indonesia

Indrarini Dyah Irawati, obtained a bachelor and master degree in Electrical Engineering at Telkom University, Bandung, Indonesia and doctoral degree in the School of Electrical and Information Engineering, Institute of Technology Bandung. She joined Telkom Applied Science School, Telkom University as an Instructor (2007-2019), Associate Professor (2019-present). Her main research interests are in the areas of compressive sensing, watermarking, signal processing, and computer network.
She is currently a member of the Association for Computing Machinery (ACM) and the International Association of Engineers (IAENG). She received Certificate of Merit from 2018 IAENG International Conference on Internet Computing and Web Services and the 2020 Best Presenter Award from International Conference on Electronics, Computer, and Communication Engineering (ICECC)

Speech Title: The Application of Compressive Sensing
Abstract: Compressive sensing / sampling (CS) is a new paradigm in the field of signal processing which has been widely applied in various applications. This theorem takes advantage of the sparse signal in the transformation region to reduce the sample size below the Shannon-Nyquist sampling rate. The main idea is that the number of information signals shows some structure or redundancy so that they can be used for signal acquisition and reconstruction simultaneously. The compressive sensing process aims to reduce the number of samples so that the data size becomes smaller, while the reconstruction process aims to restore the original data. Applying the right acquisition system will produce reconstruction results with good accuracy. Compressive sensing is widely used in several applications because it can improve system performance. In video processing and compression applications, it can significantly reduce the sampling rate. In monitoring internet network traffic, it can be used to detect traffic anomalies and reconstruct missing traffic. In a sensor network, it can co-reconstruct the intra-sensor and inter-sensor signals that come from separate measurements. Compressive sensing proves that a signal can be reconstructed spatially from information previously thought incomplete. This presentation discussed the use of CS in several applications, including monitoring internet traffic networks, telemedicine and audio watermarking.