Holistic Indexing: Offline, Online and Adaptive Indexing in the Same Kernel

Proper physical design is a momentous issue for the performance of modern database systems and applications. Nowadays, a growing amount of applications require the execution of dynamic and exploratory workloads with unpredictable characteristics that change over time, e.g., social networks, scientific databases and multime

Arabizi in Saudi Arabia:A deviant form of language or simply a form of expression?

The popularity of social networking sites in the Arab world has resulted in a new writing code, Arabizi, which combines Roman letters and numbers to represent the Arabic language. This new code received vehement criticism from Arabic linguists who argued that Arabizi is detrimental to the Arabic language and Arab identity. Arabizi use, however, has been increasing, especially in Saudi Arabia, a highly conservative and religious society. To address this apparent contradiction, this study investigated the reasons why young Saudi Arabians use Arabizi online and their attitudes towards its use. The research was based on 131 questionnaires distributed on social networking sites, and 20 interviews conducted with Saudi users of Arabizi. The findings suggest participants use Arabizi because (1), it is the language of their peers, (2) it is cool and stylish, (3) they have difficulties with the Arabic language, and (4) Arabizi constitutes a secret code, allowing escape from judgements of the older generation. The study concludes that Arabizi is a strong marker of Arab youth identity and group solidarity

Autonomous Swarm Shepherding Using Curriculum-Based Reinforcement Learning

Autonomous shepherding is a bio-inspired swarm guidance approach, whereby an artificial sheepdog guides a swarm of artificial or biological agents, such as sheep, towards a goal. While the success in this guidance depends on the set of behaviours exhibited by the sheepdog, the main source of complexity for learning effective behaviours lies within the highly non-linear dynamics featured among the swarm members as well as between the swarm and the sheepdog. Attempts to apply reinforcement learning (RL) to shepherding have so far relied greatly on rule-based algorithms for calculating waypoints to guide the RL algorithm. In this paper, we propose a curriculum-based approach for RL that does not rely on any external algorithm to pre-determine waypoints for the sheepdog. Instead, the approach uses task decomposition by formulating shepherding in terms of two sub-tasks: (1) pushing an agent from a start to a target location and (2) selecting between collecting scattered agents or driving the biggest cluster of agents to the goal. Simple-to-complex curriculum learning is used to accelerate the learning of each sub-task. For the first sub-task, the complexity is gradually increased over training time, whereas for the second sub-task a simplified environment is designed for initial learning before proceeding with the main environment. The proposed approach results in high-performance shepherding with a success rate of about 96%. While curriculum learning was found to expedite the learning of the first sub-task, it was not as efficient for the second sub-task. Our analyses highlight the need for the careful design of the curriculum to ensure that skills acquired in intermediate tasks are useful for the main tasks

Machine Education: Designing semantically ordered and ontologically guided modular neural networks

The literature on machine teaching, machine education, and curriculum design for machines is in its infancy with sparse papers on the topic primarily focusing on data and model engineering factors to improve machine learning. In this paper, we first discuss selected attempts to date on machine teaching and education. We then bring theories and methodologies together from human education to structure and mathematically define the core problems in lesson design for machine education and the modelling approaches required to support the steps for machine education. Last, but not least, we offer an ontology-based methodology to guide the development of lesson plans to produce transparent and explainable modular learning machines, including neural networks.Comment: IEEE Symposium Series on Computational Intelligence, 201

Trusted Autonomy and Cognitive Cyber Symbiosis: Open Challenges

This paper considers two emerging interdisciplinary, but related topics that are likely to create tipping points in advancing the engineering and science areas. Trusted Autonomy (TA) is a field of research that focuses on understanding and designing the interaction space between two entities each of which exhibits a level of autonomy. These entities can be humans, machines, or a mix of the two. Cognitive Cyber Symbiosis (CoCyS) is a cloud that uses humans and machines for decision-making. In CoCyS, human–machine teams are viewed as a network with each node comprising humans (as computational machines) or computers. CoCyS focuses on the architecture and interface of a Trusted Autonomous System. This paper examines these two concepts and seeks to remove ambiguity by introducing formal definitions for these concepts. It then discusses open challenges for TA and CoCyS, that is, whether a team made of humans and machines can work in fluid, seamless harmony