Methodological design and comparative evaluation of a MAS providing AmI

Researches on Ambient Intelligent and Ubiquitous Computing using wireless technologies have increased in the last years. In this work, we review several scenarios to define a multi-agent architecture that support the information needs of these new technologies, for heterogeneous domain. Our contribution consists of designing in a methodological way a Context Aware System (involving location services) using agents that can be used in very different domains. We describe all the steps followed in the design of the agent system. We apply a hybridizing methodology between GAIA and AUML. Additionally we propose a way to compare different agent architectures for Context Aware System using agent interactions. So, in this paper, we describe the assignment of weight values to agents interaction in two different MAS architectures for Context Aware problems solving different scenarios inspired in FIPA standard negotiation protocols.Publicad

Enhancing physical activity coaching through personalized motivational strategies and self-adaptive goal-setting: development of self-adaptive processes in a monitoring and coaching smartphone application

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaThe rising age of the European population brings increased costs in healthcare mainly related to the management of chronic diseases. Regular physical activity has been shown to help in the prevention and control of disease risk. Mobile phones have provided promising and emergent ways of promoting healthy lifestyles, allowing real-time monitoring and coaching to be delivered at any time and any place. The presented work adds new features to the Activity Coach, an ambulatory feedback system that aims to encourage physical activity. The Integral of the Modulus of Body Acceleration (IMA) is the unit used as an estimate for energy expenditure. Although previous research demonstrated the potential of this system, results also showed that adherence drops after a few weeks. The primary goal of this research was to design, implement, and test adaptive goal-setting and personalized feedback strategies in order to encourage physical activity. Regarding the self-adaptive goal-setting feature, the goal for each day is defined automatically based on the physical activity performed at that day of the week since the beginning of the intervention. Hence, the intention is to help the user to increase or maintain his level of physical activity taking his daily routine as a reference. The second element added to the system regards motivational feedback messages, a key factor in interventions aiming at behavior change. Based on the levels of self-efficacy, stage-of-change, and daily activity, the user is assigned to one of the six pre-defined feedback strategies in the system. The content of the motivational cues depends on the selected feedback strategy. The evaluation of the system focused on providing better understandable and more accurate feedback to the user. To do so, we evaluated the challenge and attainability of the goals provided to the user with (1) data acquired during previous studies, and (2) newly gathered data from a single-subject study. As part of the evaluation, we translated IMA counts into ‘steps’, a commonly understandable measure for physical activity, comparing the data acquired from the Activity Coach sensor with a Fitbit, a commercially available pedometer. Although further tests with more subjects and different activities should be performed, we suggest that the default values set to the system are in agreement with the Goal-Setting Theory providing challenging and attainable goals. The results from this research will be used in future experiments using the Activity Coach and can be adapted to other ambulatory feedback systems regarding promotion of physical activity

Context Aware Middleware Architectures: Survey and Challenges

Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work

Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

Privacy protection in context aware systems.

Smartphones, loaded with users’ personal information, are a primary computing device for many. Advent of 4G networks, IPV6 and increased number of subscribers to these has triggered a host of application developers to develop softwares that are easy to install on the mobile devices. During the application download process, users accept the terms and conditions that permit revelation of private information. The free application markets are sustainable as the revenue model for most of these service providers is through profiling of users and pushing advertisements to the users. This creates a serious threat to users privacy and hence it is important that “privacy protection mechanisms” should be in place to protect the users’ privacy. Most of the existing solutions falsify or modify the information in the service request and starve the developers of their revenue. In this dissertation, we attempt to bridge the gap by proposing a novel integrated CLOPRO framework (Context Cloaking Privacy Protection) that achieves Identity privacy, Context privacy and Query privacy without depriving the service provider of sustainable revenue made from the CAPPA (Context Aware Privacy Preserving Advertising). Each service request has three parameters: identity, context and actual query. The CLOPRO framework reduces the risk of an adversary linking all of the three parameters. The main objective is to ensure that no single entity in the system has all the information about the user, the queries or the link between them, even though the user gets the desired service in a viable time frame. The proposed comprehensive framework for privacy protecting, does not require the user to use a modified OS or the service provider to modify the way an application developer designs and deploys the application and at the same time protecting the revenue model of the service provider. The system consists of two non-colluding servers, one to process the location coordinates (Location server) and the other to process the original query (Query server). This approach makes several inherent algorithmic and research contributions. First, we have proposed a formal definition of privacy and the attack. We identified and formalized that the privacy is protected if the transformation functions used are non-invertible. Second, we propose use of clustering of every component of the service request to provide anonymity to the user. We use a unique encrypted identity for every service request and a unique id for each cluster of users that ensures Identity privacy. We have designed a Split Clustering Anonymization Algorithms (SCAA) that consists of two algorithms Location Anonymization Algorithm (LAA) and Query Anonymization Algorithm (QAA). The application of LAA replaces the actual location for the users in the cluster with the centroid of the location coordinates of all users in that cluster to achieve Location privacy. The time of initiation of the query is not a part of the message string to the service provider although it is used for identifying the timed out requests. Thus, Context privacy is achieved. To ensure the Query privacy, the generic queries (created using QAA) are used that cover the set of possible queries, based on the feature variations between the queries. The proposed CLOPRO framework associates the ads/coupons relevant to the generic query and the location of the users and they are sent to the user along with the result without revealing the actual user, the initiation time of query or the location and the query, of the user to the service provider. Lastly, we introduce the use of caching in query processing to improve the response time in case of repetitive queries. The Query processing server caches the query result. We have used multiple approaches to prove that privacy is preserved in CLOPRO system. We have demonstrated using the properties of the transformation functions and also using graph theoretic approaches that the user’s Identity, Context and Query is protected against the curious but honest adversary attack, fake query and also replay attacks with the use of CLOPRO framework. The proposed system not only provides \u27k\u27 anonymity, but also satisfies the \u3c k; s \u3e and \u3c k; T \u3e anonymity properties required for privacy protection. The complexity of our proposed algorithm is O(n)