Towards explainability in robotics: A performance analysis of a cloud accountability system

[EN] Understanding why a robot's behaviour was triggered is a growing concern to get human-acceptable social robots. Every action, expected and unexpected, should be able to be explained and audited. The formal model proposed here deals with different information levels, from low-level data, such as sensors' data logging; to high-level data that provide an explanation of the robot's behaviour. This study examines the impact on the robot system of a custom log engine based on a custom ROS logging node and investigates pros and cons when used together with a NoSQL database locally and in a cloud environment. Results allow to characterize these alternatives and explore the best strategy for offering a fully log-based accountability engine that maximizes the mapping between robot behaviour and robot logs.SIInstituto Nacional de CiberseguridadMinisterio de Ciencia e Innovació

Lom: discovering logic flaws within MongoDB-based web applications

Logic flaws within web applications will allow malicious operations to be triggered towards back-end database. Existing approaches to identifying logic flaws of database accesses are strongly tied to structured query language (SQL) statement construction and cannot be applied to the new generation of web applications that use not only structured query language (NoSQL) databases as the storage tier. In this paper, we present Lom, a black-box approach for discovering many categories of logic flaws within MongoDBbased web applications. Our approach introduces a MongoDB operation model to support new features of MongoDB and models the application logic as a mealy finite state machine. During the testing phase, test inputs which emulate state violation attacks are constructed for identifying logic flaws at each application state. We apply Lom to several MongoDB-based web applications and demonstrate its effectiveness

Behavioral biometrics and ambient intelligence: New opportunities for context-aware applications

Ambient Intelligence has always been associated with the promise of exciting new applications, aware of the users' needs and state, and proactive towards their goals. However, the acquisition of the necessary information for supporting such high-level learning and decision-making processes is not always straightforward. In this chapter we describe a multi-faceted smart environment for the acquisition of relevant contextual information about its users. This information, acquired transparently through the technological devices in the environment, supports the building of high-level knowledge about the users, including a quantification of aspects such as performance, attention, mental fatigue and stress. The environment described is particularly suited for milieus such as workplaces and classrooms, in which this kind of information may be very important for the effective management of human resources, with advantages for organizations and individuals alike.(UID/CEC/00319/2013)info:eu-repo/semantics/publishedVersio

Scalable Architecture for Integrated Batch and Streaming Analysis of Big Data

Thesis (Ph.D.) - Indiana University, Computer Sciences, 2015As Big Data processing problems evolve, many modern applications demonstrate special characteristics. Data exists in the form of both large historical datasets and high-speed real-time streams, and many analysis pipelines require integrated parallel batch processing and stream processing. Despite the large size of the whole dataset, most analyses focus on specific subsets according to certain criteria. Correspondingly, integrated support for efficient queries and post- query analysis is required. To address the system-level requirements brought by such characteristics, this dissertation proposes a scalable architecture for integrated queries, batch analysis, and streaming analysis of Big Data in the cloud. We verify its effectiveness using a representative application domain - social media data analysis - and tackle related research challenges emerging from each module of the architecture by integrating and extending multiple state-of-the-art Big Data storage and processing systems. In the storage layer, we reveal that existing text indexing techniques do not work well for the unique queries of social data, which put constraints on both textual content and social context. To address this issue, we propose a flexible indexing framework over NoSQL databases to support fully customizable index structures, which can embed necessary social context information for efficient queries. The batch analysis module demonstrates that analysis workflows consist of multiple algorithms with different computation and communication patterns, which are suitable for different processing frameworks. To achieve efficient workflows, we build an integrated analysis stack based on YARN, and make novel use of customized indices in developing sophisticated analysis algorithms. In the streaming analysis module, the high-dimensional data representation of social media streams poses special challenges to the problem of parallel stream clustering. Due to the sparsity of the high-dimensional data, traditional synchronization method becomes expensive and severely impacts the scalability of the algorithm. Therefore, we design a novel strategy that broadcasts the incremental changes rather than the whole centroids of the clusters to achieve scalable parallel stream clustering algorithms. Performance tests using real applications show that our solutions for parallel data loading/indexing, queries, analysis tasks, and stream clustering all significantly outperform implementations using current state-of-the-art technologies

Configuração automática de plataforma de gestão de desempenho em ambientes NFV e SDN

Mestrado em Engenharia de Computadores e TelemáticaWith 5G set to arrive within the next three years, this next-generation of mobile networks will transform the mobile industry with a profound impact both on its customers as well as on the existing technologies and network architectures. Software-Defined Networking (SDN), together with Network Functions Virtualization (NFV), are going to play key roles for the operators as they prepare the migration from 4G to 5G allowing them to quickly scale their networks. This dissertation will present a research work done on this new paradigm of virtualized and programmable networks focusing on the performance management, supervision and monitoring domains, aiming to address Self-Organizing Networks (SON) scenarios in a NFV/SDN context, with one of the scenarios being the detection and prediction of potential network and service anomalies. The research work itself was done while participating in a R&D project designated SELFNET (A Framework for Self-Organized Network Management in Virtualized and Software Defined Networks) funded by the European Commission under the H2020 5G-PPP programme, with Altice Labs being one of the participating partners of this project. Performance management system advancements in a 5G scenario require aggregation, correlation and analysis of data gathered from these virtualized and programmable network elements. Both opensource monitoring tools and customized catalog-driven tools were either integrated on or developed with this purpose, and the results show that they were able to successfully address these requirements of the SELFNET project. Current performance management platforms of the network operators in production are designed for non virtualized (non- NFV) and non programmable (non-SDN) networks, and the knowledge gathered while doing this research work allowed Altice Labs to understand how its Altaia performance management platform must evolve in order to be prepared for the upcoming 5G next generation mobile networks.Com o 5G prestes a chegar nos próximos três anos, esta próxima geração de redes móveis irá transformar a indústria de telecomunicações móveis com um impacto profundo nos seus clientes assim como nas tecnologias e arquiteturas de redes. As redes programáveis (SDN), em conjunto com a virtualização de funções de rede (NFV), irão desempenhar papéis vitais para as operadoras na sua migração do 4G para o 5G, permitindo-as escalar as suas redes rapidamente. Esta dissertação irá apresentar um trabalho de investigação realizado sobre este novo paradigma de virtualização e programação de redes, concentrando-se no domínio da gestão de desempenho, supervisionamento e monitoria, abordando cenários de redes auto-organizadas (SON) num contexto NFV/SDN, sendo um destes cenários a deteção e predição de potenciais anomalias de redes e serviços. O trabalho de investigação foi enquadrado num projeto de I&D designado SELFNET (A Framework for Self-Organized Network Management in Virtualized and Software Defined Networks) financiado pela Comissão Europeia no âmbito do programa H2020 5G-PPP, sendo a Altice Labs um dos parceiros participantes deste projeto. Avanços em sistemas de gestão de desempenho em cenários 5G requerem agregação, correlação e análise de dados recolhidos destes elementos de rede programáveis e virtualizados. Ferramentas de monitoria open-source e ferramentas catalog-driven foram integradas ou desenvolvidas com este propósito, e os resultados mostram que estas preencheram os requisitos do projeto SELFNET com sucesso. As plataformas de gestão de desempenho das operadoras de rede atualmente em produção estão concebidas para redes não virtualizadas (non-NFV) e não programáveis (non- SDN), e o conhecimento adquirido durante este trabalho de investigação permitiu à Altice Labs compreender como a sua plataforma de gestão de desempenho (Altaia) terá que evoluir por forma a preparar-se para a próxima geração de redes móveis 5G

AktiMotBot: A social media chatbot with activity tracker integration for motivating increased physical activity

The World Health Organization has reported that more than 80% of the world’s adolescent population is insufficiently physically active [1]. Up to five million deaths per year could be averted if the global population were more active[1]. The low adherence to physical activity shows the need to implement services that promote physical activity. In addition, it is crucial to educate people on the benefits of being physically active and the negative consequences sedentary behavior imposes. This thesis proposes a social media chatbot with the integration of an activity tracker that aims to motivate people to increase their daily step count. The chatbot, AktiMotBot, encourages people by implementing behavior change techniques in its messages and functionality. We use popular technology, such as social media applications, to ease access. Further, the use of chatbots has grown. A chatbot gives a service that is always available to the user and is cost-effective. In addition, chatbots have familiar interfaces that ease their use. Finally, activity data is integrated into the chatbot as a motivation and personalization tool, enabling monitoring behavior change. A thorough investigation of social media applications was conducted to ensure users’ privacy and security. A usability study investigated how potential users perceived the system, and the usability of the chatbot was scored as average. The results showed that the chatbot was able to increase the motivation of half of the participants. Finally, the findings from this research are that chatbots could motivate people to increase their physical activity levels and make people more aware of their step count

Self managed virtual machine scheduling in Cloud systems

In Cloud systems, Virtual Machines (VMs) are scheduled to hosts according to their instant resource usage (e.g. to hosts with most available RAM) without considering their overall and long-term utilization. Also, in many cases, the scheduling and placement processes are computational expensive and affect performance of deployed VMs. In this work, we present a Cloud VM scheduling algorithm that takes into account already running VM resource usage over time by analyzing past VM utilization levels in order to schedule VMs by optimizing performance. We observe that Cloud management processes, like VM placement, affect already deployed systems (for example this could involve throughput drop in a database cluster), so we aim to minimize such performance degradation. Moreover, overloaded VMs tend to steal resources (e.g. CPU) from neighbouring VMs, so our work maximizes VMs real CPU utilization. Based on these, we provide an experimental analysis to compare our solution with traditional schedulers used in OpenStack by exploring the behaviour of different NoSQL (MongoDB, Apache Cassandra and Elasticsearch). The results show that our solution refines traditional instant-based physical machine selection as it learns the system behaviour as well as it adapts over time. The analysis is prosperous as for the selected setting we approximately minimize performance degradation by 19% and we maximize CPU real time by 2% when using real world workloads