Intelligent Model of User Behavior in Distributed Systems

We present a complex neural network model of user behavior in distributed systems. The model reflects both dynamical and statistical features of user behavior and consists of three components: on-line and off-line models and change detection module. On-line model reflects dynamical features by predicting user actions on the basis of previous ones. Off-line model is based on the analysis of statistical parameters of user behavior. In both cases neural networks are used to reveal uncharacteristic activity of users. Change detection module is intended for trends analysis in user behavior. The efficiency of complex model is verified on real data of users of Space Research Institute of NASU-NSAU.

Decision Trees for Applicability of Evolution Rules in Transition P Systems

Transition P Systems are a parallel and distributed computational model based on the notion of the cellular membrane structure. Each membrane determines a region that encloses a multiset of objects and evolution rules. Transition P Systems evolve through transitions between two consecutive configurations that are determined by the membrane structure and multisets present inside membranes. Moreover, transitions between two consecutive configurations are provided by an exhaustive non-deterministic and parallel application of active evolution rules subset inside each membrane of the P system. But, to establish the active evolution rules subset, it is required the previous calculation of useful and applicable rules. Hence, computation of applicable evolution rules subset is critical for the whole evolution process efficiency, because it is performed in parallel inside each membrane in every evolution step. The work presented here shows advantages of incorporating decision trees in the evolution rules applicability algorithm. In order to it, necessary formalizations will be presented to consider this as a classification problem, the method to obtain the necessary decision tree automatically generated and the new algorithm for applicability based on it.

All-optical OFDM and distributed Raman amplification:challenges to enable high capacities and extend reach

We review recent advances in all-optical OFDM technologies and discuss the performance of a field trial of a 2 Tbit/s Coherent WDM over 124 km with distributed Raman amplification. The results indicate that careful optimisation of the Raman pumps is essential. We also consider how all-optical OFDM systems perform favourably against energy consumption when compared with alternative coherent detection schemes. We argue that, in an energy constrained high-capacity transmission system, direct detected all-optical OFDM with `ideal' Raman amplification is an attractive candidate for metro area datacentre interconnects with ~100 km fibre spans, with an overall energy requirement at least three times lower than coherent detection techniques.

Temporal Mining for Distributed Systems

Many systems and applications are continuously producing events. These events are used to record the status of the system and trace the behaviors of the systems. By examining these events, system administrators can check the potential problems of these systems. If the temporal dynamics of the systems are further investigated, the underlying patterns can be discovered. The uncovered knowledge can be leveraged to predict the future system behaviors or to mitigate the potential risks of the systems. Moreover, the system administrators can utilize the temporal patterns to set up event management rules to make the system more intelligent. With the popularity of data mining techniques in recent years, these events grad- ually become more and more useful. Despite the recent advances of the data mining techniques, the application to system event mining is still in a rudimentary stage. Most of works are still focusing on episodes mining or frequent pattern discovering. These methods are unable to provide a brief yet comprehensible summary to reveal the valuable information from the high level perspective. Moreover, these methods provide little actionable knowledge to help the system administrators to better man- age the systems. To better make use of the recorded events, more practical techniques are required. From the perspective of data mining, three correlated directions are considered to be helpful for system management: (1) Provide concise yet comprehensive summaries about the running status of the systems; (2) Make the systems more intelligence and autonomous; (3) Effectively detect the abnormal behaviors of the systems. Due to the richness of the event logs, all these directions can be solved in the data-driven manner. And in this way, the robustness of the systems can be enhanced and the goal of autonomous management can be approached. This dissertation mainly focuses on the foregoing directions that leverage tem- poral mining techniques to facilitate system management. More specifically, three concrete topics will be discussed, including event, resource demand prediction, and streaming anomaly detection. Besides the theoretic contributions, the experimental evaluation will also be presented to demonstrate the effectiveness and efficacy of the corresponding solutions.

Automatic generation of portable multi-dimensionally decomposed parallel structured mesh software

Abstract not available

Issues and strategies in the parallelisation of unstructured multiphysics code

Unstructured mesh based codes for the modelling of continuum physics phenomena have evolved to provide the facility to model complex interacting systems. Such codes have the potential to provide a high performance on parallel platforms for a small investment in programming. The critical parameters for success are to minimise changes to the code to allow for maintenance while providing high parallel efficiency, scalability to large numbers of processors and portability to a wide range of platforms. The paradigm of domain decomposition with message passing has for some time been demonstrated to provide a high level of efficiency, scalability and portability across shared and distributed memory systems without the need to re-author the code into a new language. This paper addresses these issues in the parallelisation of a complex three dimensional unstructured mesh Finite Volume multiphysics code and discusses the implications of automating the parallelisation process.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

RESULTADOS DO 13º CONTECSI USP – CONGRESSO INTERNACIONAL DE GESTÃO DA TECNOLOGIA E SISTEMAS DE INFORMAÇÃO; 36th WCARS – WORLD CONTINUOUS AUDITING AND REPORTING SYSTEMS SYMPOSIUM; 2º TOI ECA USP

In this document, we wish to describe statistics, data and the importance of the 13th CONTECSI – International Conference on Information Systems and Technology Management, which took place in the University of São Paulo, from June 1st through 3rd and was organized by TECSI/EAC/FEA/USP/ECA/POLI. This report presents statistics of the 13th CONTECSI, Goals and Objectives, Program, Plenary Sessions, Doctoral Consortium, Parallel Sessions, Honorable Mentions and Committees. We would like to point out the huge importance of the financial aid given by CAPES, CNPq, FAPESP, as well as the support of FEA USP, POLI USP, ECA USP, ANPAD, AIS, ISACA, UNINOVE, Mackenzie, Universidade do Porto, Rutgers School/USA, São Paulo Convention Bureau and CCINT-FEA-USP.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

Secure, dynamic and distributed access control stack for database applications

In database applications, access control security layers are mostly developed from tools provided by vendors of database management systems and deployed in the same servers containing the data to be protected. This solution conveys several drawbacks. Among them we emphasize: 1) if policies are complex, their enforcement can lead to performance decay of database servers; 2) when modifications in the established policies implies modifications in the business logic (usually deployed at the client-side), there is no other possibility than modify the business logic in advance and, finally, 3) malicious users can issue CRUD expressions systematically against the DBMS expecting to identify any security gap. In order to overcome these drawbacks, in this paper we propose an access control stack characterized by: most of the mechanisms are deployed at the client-side; whenever security policies evolve, the security mechanisms are automatically updated at runtime and, finally, client-side applications do not handle CRUD expressions directly. We also present an implementation of the proposed stack to prove its feasibility. This paper presents a new approach to enforce access control in database applications, this way expecting to contribute positively to the state of the art in the field.

Secure, dynamic and distributed access control stack for database applications

In database applications, access control security layers are mostly developed from tools provided by vendors of database management systems and deployed in the same servers containing the data to be protected. This solution conveys several drawbacks. Among them we emphasize: (1) if policies are complex, their enforcement can lead to performance decay of database servers; (2) when modifications in the established policies implies modifications in the business logic (usually deployed at the client-side), there is no other possibility than modify the business logic in advance and, finally, 3) malicious users can issue CRUD expressions systematically against the DBMS expecting to identify any security gap. In order to overcome these drawbacks, in this paper we propose an access control stack characterized by: most of the mechanisms are deployed at the client-side; whenever security policies evolve, the security mechanisms are automatically updated at runtime and, finally, client-side applications do not handle CRUD expressions directly. We also present an implementation of the proposed stack to prove its feasibility. This paper presents a new approach to enforce access control in database applications, this way expecting to contribute positively to the state of the art in the field.

Elastic Computing - a novel paradigm for distributed systems

This talk, which is based on our newest findings and experiences from research and industrial projects, addresses one of the most relevant challenges for a decade to come: How to integrate the Internet of Things with software, people, and processes, considering modern Cloud Computing and Elasticity principles. Elasticity is seen as one of the main characteristics of Cloud Computing today. Is elasticity simply scalability on steroids? This talk addresses the main principles of elasticity, presents a fresh look at this problem, and examines how to integrate people, software services, and things into one composite system, which can be modeled, programmed, and deployed on a large scale in an elastic way. This novel paradigm has major consequences on how we view, build, design, and deploy ultra-large scale distributed systems.

Interference mitigation techniques for cloud-RAN deployments in 5G and beyond systems

The deployment of ultra-dense networks is one of the most promising solutions to manage the phenomenon of co-channel interference that affects the latest wireless communication systems, especially in hotspots. To meet the requirements of the use-cases and the immense amount of traffic generated in these scenarios, 5G ultra-dense networks are being deployed using various technologies, such as distributed antenna system (DAS) and cloud-radio access network (C-RAN). Through these centralized densification schemes, virtualized baseband processing units coordinate the distributed access points and manage the available network resources. In particular, link adaptation techniques are shown to be fundamental to overall system operation and performance enhancement. The core of this dissertation is the result of an analysis and a comparison of dynamic and adaptive methods for modulation and coding scheme (MCS) selection applied to the latest mobile telecommunications standards. A novel algorithm based on the proportional-integral-derivative (PID) controller principles and block error rate (BLER) target has been proposed. Tests were conducted in a 4G and 5G system level laboratory and, by means of a channel emulator, the performance was evaluated for different channel models and target BLERs. Furthermore, due to the intrinsic sectorization of the end-users distribution in the investigated scenario, a preliminary analysis on the joint application of users grouping algorithms with multi-antenna and multi-user techniques has been performed. In conclusion, the importance and impact of other fundamental physical layer operations, such as channel estimation and power control, on the overall end-to-end system behavior and performance were highlighted.

Power Transformer Differential Protection Based on Clarke`s Transform and Fuzzy Systems

The power transformer is a piece of electrical equipment that needs continuous monitoring and fast protection since it is very expensive and an essential element for a power system to perform effectively. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can affect the protection behavior and the power system stability. This paper proposes the development of a new algorithm to improve the differential protection performance by using fuzzy logic and Clarke`s transform. An electrical power system was modeled using Alternative Transients Program (ATP) software to obtain the operational conditions and fault situations needed to test the algorithm developed. The results were compared to a commercial relay for validation, showing the advantages of the new method.

Tropical woods: influence of density in hardness parallel and normal to the grain for some Brazilian tropical tree species

The aim of this work is to verify the possibility to correlating specific gravity and wood hardness parallel and perpendicular to the grain. The purpose is to offer one more tool to help in the decision about wood species choice for use in floors and sleepers. To reach this intent, we considered the results of standard tests (NBR 7190:1997, Timber Structures Design, Annex B, Brazilian Association of Technical Standards) to determine hardness parallel and normal to the grain in fourteen tropical high density wood species (over 850 kg/m(3), at 12% moisture content). For each species twelve determinations were made, based on the material obtained at Sao Carlos and its regional wood market. Statistical analysis led to some expressions to describe the cited properties relationships, with a determination coefficient about 0.8.