Pedro Alves

Bio: Pedro Alves is an academic researcher from Technical University of Lisbon. The author has contributed to research in topics: Context (language use) & Scalability. The author has an hindex of 4, co-authored 8 publications receiving 33 citations. Previous affiliations of Pedro Alves include INESC-ID & Instituto Superior Técnico.

How to Get a Badge? Unlock Your Mind : Motivation through Student Empowerment

Abstract: The introduction of gamification principles into the classroom may enhance and extend the student engagement in learning activities, and therefore, contribute to a more efficient knowledge acquisition. In this paper, we report a case study whose methodology is based on awarding merit badges as a result of the successful completion of learning activities focused on either soft and hard skills. This initiative was implemented in two universities, involving 221 participants, aimed at assessing how the proposed model may contribute to students’ motivation, appealing for its participation in the classroom, and measuring their stimulation for continuous study. Early stage results are promising and suggest not only a higher acceptance of the system and its effectiveness, but also, the suitability of gamification and reward systems on education. Further studies should be implemented in order to determine the effect of the reward system on students’ grades.

Distributed Context-Aware Systems

Abstract: Context-aware systems aim to deliver a rich user experience by taking intoaccount the current user context (location, time, activity, etc.), possiblycaptured without his intervention. For example, cell phones are now able tocontinuously update a users location while, at the same time, users executean increasing amount of activities online, where their actions may be easilycaptured (e.g. login in a web application) without user consent. In the last decade, this topic has seen numerous developments that demonstrate its relevance and usefulness. Thetrend was accelerated with the widespreadavailability of powerful mobile devices (e.g. smartphones) that include a myriad ofsensors which enable applications to capture the user context. However, there are several challenges that must be addressed; we focus on scalability(large number of context aware messages) and privacy (personal data that may be propagated).This book is organized in five chapters starting with an introduction tothe theme raising the most important challenges. Then, chapter two presents several importantdefinitions (establishing a common ground for the following chapters) andtaxonomy. Theseare important to chapter three which describes some of the most relevant distributedcontext-aware systems that can be classified according to the taxonomy. Privacy is addressedin chapter four andchapter fivepresents some important conclusions. The audience for this book is wide; researchers, students and professionals interested inthe areas addressed will find the most relevant information regarding scalability and privacy indistributed context-aware systems.

AnonyLikes: Anonymous Quantitative Feedback on Social Networks

Abstract: Social network applications (SNAs) can have a tremendous impact in raising awareness to important controversial topics such as religion or politics. Sharing and liking are powerful tools to make some of those topics emerge to a global scale, as already witnessed in the recent Tunisian and Egyptian revolutions.

Radiator - efficient message propagation in context-aware systems

Abstract: Applications such as Facebook, Twitter and Foursquare have brought the mass adoption of personal short messages, distributed in (soft) real-time on the Internet to a large number of users. These messages are complemented with rich contextual information such as the identity, time and location of the person sending the message (e.g., Foursquare has millions of users sharing their location on a regular basis, with almost 1 million updates per day). Such contextual messages raise serious concerns in terms of scalability and delivery delay; this results not only from their huge number but also because the set of user recipients changes for each message (as their interests continuously change), preventing the use of well-known solutions such as pub-sub and multicast trees. This leads to the use of non-scalable broadcast based solutions or point-to-point messaging. We propose Radiator, a middleware to assist application programmers implementing efficient context propagation mechanisms within their applications. Based on each user’s current context, Radiator continuously adapts each message propagation path and delivery delay, making an efficient use of network bandwidth, arguably the biggest bottleneck in the deployment of large-scale context propagation systems. Our experimental results demonstrate a 20x reduction on consumed bandwidth without affecting the real-time usefulness of the propagated messages.

ReConMUC: adaptable consistency requirements for efficient large-scale multi-user chat

Abstract: Multi-user chat (MUC) applications raise serious challenges to developers concerning scalability and efficient use of network bandwidth, due to a large number of users exchanging lots of messages in real-time. We propose a new approach to MUC message propagation based on an adaptable consistency model bounded by three metrics: Filter, Time and Volume. In this model, the server propagates some messages as soon as possible while others are postponed until certain conditions are met, according to each client consistency requirements. These requirements can change during the session lifetime, constantly adapting to each client's current context. We developed a prototype called ReConMUC (Relaxed Consistency MUC) as an extension to a well-known MUC protocol, which, by attaching a special component to the server, filters messages before they are broadcast, according to client consistency requirements. The performance results obtained show that ReConMUC effectively reduces the server outbound bandwidth, without significant increase in memory and CPU usage, thus improving scalability.

Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education

Abstract: Mobile game-based learning constitutes a hot issue in the related scientific literature since it promotes learning through an entertaining way and fosters student motivation to increase engagement in the educational process. As such, it can enhance the learning process and improve student participation. Towards this direction, this paper investigates how mobile learning and game-based learning can be utilized in higher education settings and analyzes the pedagogical affordance of their adoption. As a testbed for our research, we designed and implemented Quiz Time! which is an intelligent mobile game-based learning application for assessing and advancing learners' knowledge in the programming language C#. Quiz Time! employs an assessing knowledge module for testing the knowledge of learners, a vectorial-based recommendation module for proposing personalized collaboration in group playing, a dynamic fuzzy logic-based advice generator for tailored assistance to learners' profile and misconceptions, and a cognitive learner modeler supporting the aforementioned modules. Quiz Time! was used in a higher education institution for an academic semester and was evaluated by students and computer science experts using an established framework and the statistical hypothesis test. Regarding the evaluation results, the computer science experts validated the pedagogical adequacy of the application and the students highlighted its positive impact on learning and its usefulness. A major conclusion is that incorporating personalization and collaboration in mobile game-based learning can further assist students in higher education towards advancing their knowledge level.

The New War Correspondents: the Rise of Civic Media Curation in Urban Warfare

Abstract: In this paper we examine the information sharing practices of people living in cities amid armed conflict. We describe the volume and frequency of microblogging activity on Twitter from four cities afflicted by the Mexican Drug War, showing how citizens use social media to alert one another and to comment on the violence that plagues their communities. We then investigate the emergence of civic media "curators," individuals who act as "war correspondents" by aggregating and disseminating information to large numbers of people on social media. We conclude by outlining the implications of our observations for the design of civic media systems in wartime.

SEEN: A Selective Encryption Method to Ensure Confidentiality for Big Sensing Data Streams

Abstract: Resource constrained sensing devices are being used widely to build and deploy self-organizing wireless sensor networks for a variety of critical applications such as smart cities, smart health, precision agriculture and industrial control systems. Many such devices sense the deployed environment and generate a variety of data and send them to the server for analysis as data streams. A Data Stream Manager (DSM) at the server collects the data streams (often called big data) to perform real time analysis and decision-making for these critical applications. A malicious adversary may access or tamper with the data in transit. One of the challenging tasks in such applications is to assure the trustworthiness of the collected data so that any decisions are made on the processing of correct data. Assuring high data trustworthiness requires that the system satisfies two key security properties: confidentiality and integrity. To ensure the confidentiality of collected data, we need to prevent sensitive information from reaching the wrong people by ensuring that the right people are getting it. Sensed data are always associated with different sensitivity levels based on the sensitivity of emerging applications or the sensed data types or the sensing devices. For example, a temperature in a precision agriculture application may not be as sensitive as monitored data in smart health. Providing multilevel data confidentiality along with data integrity for big sensing data streams in the context of near real time analytics is a challenging problem. In this paper, we propose a Selective Encryption (SEEN) method to secure big sensing data streams that satisfies the desired multiple levels of confidentiality and data integrity. Our method is based on two key concepts: common shared keys that are initialized and updated by DSM without requiring retransmission, and a seamless key refreshment process without interrupting the data stream encryption/decryption. Theoretical analyses and experimental results of our SEEN method show that it can significantly improve the efficiency and buffer usage at DSM without compromising the confidentiality and integrity of the data streams.

Software Engineering Education: Challenges and Perspectives

Abstract: The software engineering community celebrated, in 2018, the 50th anniversary of what is considered to be the official start of the profession of software engineering. Software engineering is a young and promising discipline which is still under development and improvement. This is reflected when teaching software engineering in higher education. The aim of this study is to investigate the challenges and perspectives of software engineering education. To do so, a questionnaire study was conducted. 21 software engineering faculty and experts in teaching software engineering related courses participated in this study. The questionnaire contained demographic questions, questions related to students’ engagement and to different methodologies adopted by respondents in the classroom. Results showed that the majority of respondents found engaging students in software engineering courses to be the biggest challenge they faced in the classroom. Almost half of the participants found difficulties designing practical activities for students. Results also revealed that the problem-based learning approach is the most used in software engineering lectures, followed by gamification techniques and role-playing which are new trends used to engage students. Moreover, the majority of the participants considered that the adoption of new teaching methodologies in the classroom produced high impact in the students’ learning experience. Based on the outcomes of this questionnaire study, a conceptual model to engage students in software engineering courses is proposed. For future work, complementary studies should be implemented to evaluate the proposed model in a real-world scenarios including its effect on the achievement of learning outcomes.

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

Abstract: Autonomous vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, advanced-vehicle systems (AVSs) need to be better contextually aware. The vast literature referring to “context-aware systems” is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realize contextual awareness in AVSs. This paper uses a human-AVSs (HAVSs) lens to polarize the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextual-awareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human–machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.