As the world is flooded with deluge of data, the demand for mining data to gain insights is increasing. One effective technique to deal with the problem is to model the data as networks (graphs) and then apply graph mining techniques to uncover useful patterns. Several graph mining techniques have been studied in the literature, and graphlet-based analysis is gaining popularity due to its power in exposing hidden structure and interaction within the networks.

Growing population, urbanization, increased sophistication in life, global warming, and climate change are some of the factors that can worsen the scarcity and quality of water in coming decades. Therefore, a sustainable water governance is essential across the world to face this challenge. In this paper, we study two water governance networks constructed from ground-truth using a new network analysis technique called graphlet analysis. Graphlet is gaining popularity in network analysis due to its power in exposing network structure and functions. To the best of our knowledge, this is the first work to apply graphlet analysis to study water governance network.

Since the introduction of microarray technology, collecting and analyzing gene expression data to identify biomarkers for clinical applications such as diagnosis, prognosis, and/or prediction of suitable treatment has been widely practiced. Yet the progress is slow and limited. In this paper, we propose a new and relatively simple technique based on signed graphlets to reduce gene expression data of any large size to a constant size heatmap. The heatmaps obtained from gene expression data collected under various conditions, we believe, could be used to derive biomarkers or could be used themselves as biomarkers of different cellular states. A database of such biomarker signatures could increase the accuracy and efficacy of the clinical applications of diagnosis, prognosis, and hence choosing suitable treatment options. We demonstrate our approach by analyzing gene expression data obtained from three types of cell samples - one from healthy cells and two from two types of cancer cells. The initial results are encouraging. As a test of robustness, we repeated the computation with 6 sets of random samples from each type and constructed the heatmaps. We observe that, within each sample type, the heatmaps are noticeably invariant.

Recent advancement of artificial intelligence (AI) techniques have impacted the field of algorithmic music composition, and that has been evidenced by concert performances wherein the audience reportedly often could not tell whether music was composed by machine or by human. Among the AI techniques, genetic algorithms dominate the field due to their suitability for both creativity and optimization. Recently, many attempts have been made to incorporate rules from traditional music theory to design and automate genetic algorithms. However, due to the exclusive use of a very constrained set of traditional music rules and their primitive nature, the field seems to face stagnation. This paper is aimed at addressing the above limitation and hence paving the way to advance the field towards composing human-quality music. The basic idea is to look beyond this constrained set of traditional music rules towards music universals and music-psychology. To demonstrate the proposed approach, we implemented a prototype genetic algorithm and conducted a limited experiment to test the quality of its composition. The initial result is interesting and encouraging.

Designing efficient control strategies is well studied. Due to recent technological advancements and applications to the field of robotics, exploring ways to design optimal control for multi robot systems is gaining interest. In this respect, ergodicity has been successfully applied as an effective control technique for tracking and coverage. Generating flocking behaviour is a problem with many interesting special cases that include both tracking and coverage. The main contribution of this paper is the application of ergodicity to emulate flocking behaviour. Our approach is appealing, as control and communication are all assumed to be local, and so this behaviour is self-organized. Simulation results show that the proposed approach is effective.

Traffic congestion is a growing concern in most cities across the world. It is primarily caused by a sudden increase in the number of vehicles in a relatively small number of roads and intersections, while other roads have the capacity to accommodate more traffic. In such situations, distributing traffic to roads in a balanced way could alleviate congestion. With the help of modern technology such as Internet of Things (IoT) and simulation, road users can be encouraged to choose their route on-the-fly, by providing necessary information such as projected travel time on the next leg. In extreme situations, traffic on some critical roads could be adaptively reduced by even introducing levy. A simple solution like providing road traffic information, benefits and penalties, etc., ahead in each intersection would allow travellers to make cognizant choices and therefore could lead to a better, more efficient traffic distribution.

Operating systems is a central subject in computer science curriculum, and teaching it successfully has been a challenge. Effective learning requires carefully designed programming projects that can facilitate deep thinking and enhance hands-on experience. This paper presents MiniOS, an instructional embedded operating system designed to carry out operating systems projects successfully. We explain MiniOS approach, the rationale behind it, and compare it with other similar approaches.

As multicore processors are becoming the norm, an efficient scheduling of cores to the threads is fundamentally important for multicore computing. To study the performance of a new scheduling algorithm for the future multicore systems with hundreds and thousands of cores, we need a flexible scheduling simulation testbed. Designing such a multicore scheduling simulation testbed and illustrating its functionality are the main contributions of this paper. The proposed scheduling simulation testbed is developed using Java and expected to be released for public use.

As multicore processors are becoming increasingly common everywhere, the future computing systems and devices are becoming inevitably concurrent. Also, on the applications side, automation is steadily infiltrating into everyday life, and hence, most software systems are becoming increasingly complex and concurrent. As a result, recent developments and projections indicate that we are entering into the era of concurrent programming. Synchronizing asynchronous concurrent processes in accessing a shared resource is an important issue. Among the synchronization issues, mutual exclusion is fundamental. Solutions to most higher level synchronization problems rely on the assurance of mutual exclusion. Several algorithms with varying characteristics are proposed in the literature to solve the mutual exclusion problem. This paper presents two new algorithms to solve the mutual exclusion problem. The algorithms are simple and have many nice properties.

The paper presents several new protocol classes for mobile wireless ad-hoc networks that rely on specific mobility patterns of router nodes in order to guarantee tight upper bounds on communication delays. We introduce a generic formal model of a heterogeneous network in which regular nodes exchange messages through a subnetwork of dedicated router nodes, and use it to identify desirable properties of router mobility patterns. In particular, the analysis focusses on protocols that periodically cover ('sweep') the entire communication zone. We define specific classes of sweep protocols and prove the upper bounds on router contact delays and on end-to-end message delivery times that they guarantee to stationary or mobile populations of regular nodes. The results of simulation experiments show that a translatory chain sweep protocol outperforms the runners protocol with respect to end-to-end message delivery times for both stationary and mobile regular nodes.