Overall quality optimization for DQM stage in High Energy Physics experiments

Data Acquisition (DAQ) and Data Quality Monitoring (DQM) are key parts in the HEP data chain, where the data are processed and analyzed to obtain accurate monitoring quality indicators. Such stages are complex, including an intense processing work-flow and requiring a high degree of interoperability between software and hardware facilities. Data recorded by DAQ sensors and devices are sampled to perform live (and offline) DQM of the status of the detector during data collection providing to the system and scientists the ability to identify problems with extremely low latency, minimizing the amount of data that would otherwise be unsuitable for physical analysis. DQM stage performs a large set of operations (Fast Fourier Transform (FFT), clustering, classification algorithms, Region of Interest, particles tracking, etc.) involving the use of computing resources and time, depending on the number of events of the experiment, sampling data, complexity of the tasks or the quality performance. The objective of our work is to show a proposal with aim of developing a general optimization of the DQM stage considering all these elements. Techniques based on computational intelligence like EA can help improve the performance and therefore achieve an optimization of task scheduling in DQM.(MINECO - Gov. of Spain) P12-TIC-2958 TIN2016-81113-

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed

KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system