Concept of Islamic Leadership in Syed Muhammad Naquib Al-Attas’s Philosophy

Islamic leadership is defined as a divine trust (amanah) from Allah, based on the Tauhidic paradigm of oneness of Allah that is focusing to serve HIM as HIS servant, while at the same time be the servant of Allah, perform the role of Khalefah (vicegerent) of Allah fully adheres to the Sunnah central to Islamic personality, characteristics and aklaq (Islamic moral and ethical) that was exemplified by the prophet (s.a.w), leading followers to the organization goals and to the straight path of the success in the Hereafter. It is based on this adopted definition that the paper tries to look into the notion from perspective of a Malaysian Muslim Philosopher to the core, Syed Muhammad Naquib Al-Attas, who believes in delivering amanah given to him, and at the same time perform the role of vicegerent of Allah. He suggests that lack of good leaders or rise of unqualified and false ones is one of the major problems of Ummah. Content analysis approach is engaged on both primary and secondary data collected. That is to say, it is a qualitative research in nature. The finding shows that the basis of Al-Attas’s concept of leadership is entrenched in Al-Qur’an and the tradition of the prophet in addition to his deep understanding on the Islamic Sciences such as Islamic theology, philosophy, and metaphysis and so on.

Robust Algorithm for Genome Sequence Short Read Error Correction Using Hadoop-Mapreduce

Biological sequences consist of A C G and T in a DNA structure and contain vital information of living organisms. The development of computing technologies, especially NGS technologies have increased genomic data at a rapid rate. The increase in genomic data presents significant research challenges in bioinformatics, such as sequence alignment, short-reads error correction, phylogenetic inference, etc. Next-generation high-throughput sequencing technologies have opened new and thought-provoking research opportunities. In particular, Next-generation sequencers produce a massive amount of short-reads data in a single run. However, these large amounts of short-reads data produced are highly susceptible to errors, as compared to shotgun sequencing. Therefore, there is a peremptory demand to design fast and more accurate statistical and computational tools to analyze these data. This research presents a novel and robust algorithm called HaShRECA for genome sequence short reads error correction. The developed algorithm is based on a probabilistic model that analyzes the potential errors in reads and utilizes the Hadoop-MapReduce framework to speed up the computation processes. Experimental results show that HaShRECA is more accurate, as well as time and space efficient as compared to previous algorithms.