Abstract
The increasing level of sophistication of cyber attacks which are employing cross-cutting strategies that leverage multi-domain attack surfaces, including but not limited to, software defined networking poisoning, biasing of machine learning models to suppress detection, exploiting software (development), and leveraging system design deficiencies. While current defensive solutions exist, they only partially address multi-domain and multi-stage attacks, thus rendering them ineffective to counter the upcoming generation of attacks. More specifically, we argue that a disruption is needed to approach separated knowledge domains, namely Intrusion Tolerant systems, cybersecurity, and machine learning. We argue that current solutions tend to address different concerns/facets of overlapping issues and they tend to make strong assumptions of supporting infrastructure, e.g., assuming that event probes/metrics are not compromised. To address these issues, we present Skynet, a platform that acts as a secure overseer that merges traditional roles of SIEMs with conventional orchestrators while being rooted on the fundamentals introduced by previous generations of intrusion tolerant systems. Our goal is to provide an open-source intrusion tolerant platform that can dynamically adapt to known and unknown security threats in order to reduce potential vulnerability windows.

Abstract
Users increasingly own, and use, multiple computing devices. To be able to access their personal data, at any time and in any device, users usually need to create replicas in each device. Managing these multiple replicas becomes an important issue. In this paper we present the FEW Phone File System, a data management system that combines mobile and cloud storage for providing ubiquitous data access. To this end, our system takes advantage of the characteristics of mobile phones for storing a replica of a user's personal data, thus allowing these devices to be used as personal and portable file servers. As users tend to always carry their mobile phone with them at all times, these replicas are the basis for providing high data availability, and keeping replicas automatically synchronized. Our system also uses other replicas located in web servers and cloud storage systems, to reduce the volume of data stored, and transferred to/from mobile phones, by maintaining only the information needed to obtain them.

Abstract
Road condition has an important role in our daily live. Anomalies in road surface can cause accidents, mechanical failure, stress and discomfort in drivers and passengers. Governments spend millions each year in roads maintenance for maintaining roads in good condition. But extensive maintenance work can lead to traffic jams, causing frustration in road users. In way to avoid problems caused by road anomalies, we propose a system that can detect road anomalies using smartphone sensors. The approach is based in data-mining algorithms to mitigate the problem of hardware diversity. In this work we used scikit-learn, a python module, and Weka, as tools for data-mining. All cleaning data process was made using python language. The fmal results show that it is possible detect road anomalies using only a smartphone. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
Multicore processors are available for over a decade, but general purpose database management systems (DBMS) still cannot fully explore the computational resources of these platforms. This paper explores a simple and easy to deploy approach for improving DBMS performance in multicore platforms, by maintaining multiple database engines running in parallel, rather than a single instance, thus circumventing the increase in contention due to load interactions. Unlike previous works, we focus on in-memory DBMS, exploring different design solutions that combine distributed systems and concurrent programming techniques. We show that we are able to improve performance over standalone solutions, without modifying either database or application code, by up to 3 times while minimizing response times.

Abstract
Programs increasingly rely on the use of complex component libraries, such as in-memory databases. As any other software, these libraries have bugs that may lead to the application failure. In this work we revisit the idea of software component replication for masking software bugs in the context of multi-core systems. We propose a new abstraction: a Macro-Component. A Macro-Component is a software component that includes several internal replicas with diverse implementations to detect and mask bugs. By relying on modern multicores processing capacity it is possible to execute the same operation in multiple replicas concurrently, thus incurring in minimal overhead. Also, by exploring the multiple existent implementations of well-known interfaces, it is possible to use the idea without incurring in additional development cost. © 2013 Springer-Verlag.