Abstract
This paper describes an energy management system that is being developed in the AnyPLACE project to support new energy services, like demand response, in residential buildings. In the project end-user interfaces are designed and implemented to allow the input of preferences regarding the flexible use of shiftable and thermal appliances. Monitoring and self-learning algorithm are used to allow additional information to be collected and an automation platform is available for the management and control of appliances. An energy management algorithm is presented that processes end-user preferences and devices characteristics to produce an optimal dispatch considering demand response incentives. Results show the successful implementation of an optimized energy scheduling.

Abstract
The privacy of information is an increasing concern of software applications users. This concern was caused by attacks to cloud services over the last few years, that have leaked confidential information such as passwords, emails and even private pictures. Once the information is leaked, the users and software applications are powerless to contain the spread of information and its misuse. With databases as a central component of applications that store almost all of their data, they are one of the most common targets of attacks. However, typical deployments of databases do not leverage security mechanisms to stop attacks and do not apply cryptographic schemes to protect data. This issue has been tackled by multiple secure databases that provide trade-offs between security, query capabilities and performance. Despite providing stronger security guarantees, the proposed solutions still entrust their data to a single entity that can be corrupted or hacked. Secret sharing can solve this problem by dividing data in multiple secrets and storing each secret at a different location. The division is done in such a way that if one location is hacked, no information can be leaked. Depending on the protocols used to divide data, functions can be computed over this data through secure protocols that do not disclose information or actually know which values are being calculated. We propose a SQL database prototype capable of offering a trade-off between security and query latency by using a different secure protocol. An evaluation of the protocols is also performed, showing that our most relaxed protocol has an improvement of 5% on the query latency time over the original protocol. © 2017 ACM.

Abstract
Keeping replicated data strongly consistent is convenient when communication is fast and available. In internet-scale distributed systems the reality of high communication latencies and likelihood of partitions, leads developers to adopt more relaxed consistency models, such as eventual consistency. Conflict-free Replicated Data Types, bring structure to the design of eventually consistent data management solutions, by precisely describing the behaviour under concurrent updates and guarantying a path to reconciliation. This paper offers a survey of the mathematical structures that support state based multi-master replication with reconciliation, and shows how state structures and state transformations can be composed to provide data types that are now used in practice in many geo-replicated systems.

Abstract
In the framework of the Horizon 2020 project UPGRID, the Portuguese demo is focused on promoting the exchange of smart metering data between the DSO and different stakeholders, guaranteeing neutrality, efficiency and transparency. The platform described in this study, named the Market Hub Platform, has two main objectives: (i) to guarantee neutral data access to all market agents and (ii) to operate as a market hub for the home energy management systems flexibility, in terms of consumption shift under dynamic retailing tariffs and contracted power limitation requests in response to technical problems. The validation results are presented and discussed in terms of scalability, availability and reliability.

Abstract
The exponential growth of data produced, the ever faster and ubiquitous connectivity, and the collaborative processing tools lead to a clear shift of data stores from local servers to the cloud. This migration occurring across different application domains and types of users|individual or corporate|raises two immediate challenges. First, outsourcing data introduces security risks, hence protection mechanisms must be put in place to provide guarantees such as privacy, confidentiality and integrity. Second, there is no \one-size-fits-all" solution that would provide the right level of safety or performance for all applications and users, and it is therefore necessary to provide mechanisms that can be tailored to the various deployment scenarios. In this paper, we address both challenges by introducing SafeFS, a modular architecture based on software-defined storage principles featuring stackable building blocks that can be combined to construct a secure distributed file system. SafeFS allows users to specialize their data store to their specific needs by choosing the combination of blocks that provide the best safety and performance tradeoffs. The file system is implemented in user space using FUSE and can access remote data stores. The provided building blocks notably include mechanisms based on encryption, replication, and coding. We implemented SafeFS and performed indepth evaluation across a range of workloads. Results reveal that while each layer has a cost, one can build safe yet efficient storage architectures. Furthermore, the different combinations of blocks sometimes yield surprising tradeoffs. © 2017 ACM.

Abstract
Pose invariant object detection and classification plays a critical role in robust image recognition systems and can be applied in a multitude of applications, ranging from simple monitoring to advanced tracking. This paper analyzes the usage of the Bag of Words model for recognizing objects in different scales, orientations and perspective views within cluttered environments. The recognition system relies on image analysis techniques, such as feature detection, description and clustering along with machine learning classifiers. For pinpointing the location of the target object, it is proposed a multiscale sliding window approach followed by a dynamic thresholding segmentation. The recognition system was tested with several configurations of feature detectors, descriptors and classifiers and achieved an accuracy of 87% when recognizing cars from an annotated dataset with 177 training images and 177 testing images. © Springer International Publishing AG 2018.