Abstract: Data storage is a major and growing part of IT budgets for research since manyyears. Especially in biology, the amount of raw data products is growing continuously,and the advent of the so-called "next-generation" sequencers has made things worse.Affordable prices have pushed scientists to massively sequence whole genomes and to screenlarge cohort of patients, thereby producing tons of data as a side effect. The need formaximally fitting data into the available storage volumes has encouraged and welcomednew compression algorithms and tools. We focus here on state-of-the-art compression toolsand measure their compression performance on ABI SOLiD data.
Abstract: In this contribution, a generic two-phase stochastic variable neighborhood approach is applied to nurse rostering problems. The proposed algorithm is used for creating feasible and efficient nurse rosters for many different nurse rostering cases. In order to demonstrate the efficiency and generic applicability of the proposed approach, experiments with real-world input data coming from many different nurse rostering cases have been conducted. The nurse rostering instances used have significant differences in nature, structure, philosophy and the type of hard and soft constraints. Computational results show that the proposed algorithm performs better than six different existing approaches applied to the same nurse rostering input instances using the same evaluation criteria. In addition, in all cases, it manages to reach the best-known fitness achieved in the literature, and in one case, it manages to beat the best-known fitness achieved till now.
Abstract: This paper is motivated by, but not limited to, the task of scheduling jobs organized in workflows to a computational grid. Due to the dynamic nature of grid computing, more or less permanent replanning is required so that only very limited time is available to come up with a revised plan. To meet the requirements of both users and resource owners, a multi-objective optimization comprising execution time and costs is needed. This paper summarizes our work over the last six years in this field, and reports new results obtained by the combination of heuristics and evolutionary search in an adaptive Memetic Algorithm. We will show how different heuristics contribute to solving varying replanning scenarios and investigate the question of the maximum manageable work load for a grid of growing size starting with a load of 200 jobs and 20 resources up to 7000 jobs and 700 resources. Furthermore, the effect of four different local searchers incorporated into the evolutionary search is studied. We will also report briefly on approaches that failed within the short time frame given for planning.
Abstract: Course timetabling is a combinatorial optimization problem and has been confirmed to be an NP-complete problem. Course timetabling problems are different for different universities. The studied university course timetabling problem involves hard constraints such as classroom, class curriculum, and other variables. Concurrently, some soft constraints need also to be considered, including teacher’s preferred time, favorite class time etc. These preferences correspond to satisfaction values obtained via questionnaires. Particle swarm optimization (PSO) is a promising scheme for solving NP-complete problems due to its fast convergence, fewer parameter settings and ability to fit dynamic environmental characteristics. Therefore, PSO was applied towards solving course timetabling problems in this work. To reduce the computational complexity, a timeslot was designated in a particle’s encoding as the scheduling unit. Two types of PSO, the inertia weight version and constriction version, were evaluated. Moreover, an interchange heuristic was utilized to explore the neighboring solution space to improve solution quality. Additionally, schedule conflicts are handled after a solution has been generated. Experimental results demonstrate that the proposed scheme of constriction PSO with interchange heuristic is able to generate satisfactory course timetables that meet the requirements of teachers and classes according to the various applied constraints.
Abstract: The Internet of Things (IoT) refers to the Internet-like structure of billions of interconnected constrained devices, denoted as “smart objects”. Smart objects have limited capabilities, in terms of computational power and memory, and might be battery-powered devices, thus raising the need to adopt particularly energy efficient technologies. Among the most notable challenges that building interconnected smart objects brings about, there are standardization and interoperability. The use of IP has been foreseen as the standard for interoperability for smart objects. As billions of smart objects are expected to come to life and IPv4 addresses have eventually reached depletion, IPv6 has been identified as a candidate for smart-object communication. The deployment of the IoT raises many security issues coming from (i) the very nature of smart objects, e.g., the adoption of lightweight cryptographic algorithms, in terms of processing and memory requirements; and (ii) the use of standard protocols, e.g., the need to minimize the amount of data exchanged between nodes. This paper provides a detailed overview of the security challenges related to the deployment of smart objects. Security protocols at network, transport, and application layers are discussed, together with lightweight cryptographic algorithms proposed to be used instead of conventional and demanding ones, in terms of computational resources. Security aspects, such as key distribution and security bootstrapping, and application scenarios, such as secure data aggregation and service authorization, are also discussed.
Abstract: Portfolio optimization is one of the problems most frequently encountered by financial practitioners. The main goal of this paper is to fill a gap in the literature by providing a well-documented, step-by-step open-source implementation of Critical Line Algorithm (CLA) in scientific language. The code is implemented as a Python class object, which allows it to be imported like any other Python module, and integrated seamlessly with pre-existing code. We discuss the logic behind CLA following the algorithm’s decision flow. In addition, we developed several utilities that support finding answers to recurrent practical problems. We believe this publication will offer a better alternative to financial practitioners, many of whom are currently relying on generic-purpose optimizers which often deliver suboptimal solutions. The source code discussed in this paper can be downloaded at the authors’ websites (see Appendix).
