Possibilistic networks: MAP query and computational analysis

International audienc

In-Network Decision Making Intelligence for Task Allocation in Edge Computing

Humongous contextual data are produced by sensing and computing devices (nodes) in distributed computing environments supporting inferential/predictive analytics. Nodes locally process and execute analytics tasks over contextual data. Demanding inferential analytics are crucial for supporting local real-time applications, however, they deplete nodes' resources. We contribute with a distributed methodology that pushes the task allocation decision at the network edge by intelligently scheduling and distributing analytics tasks among nodes. Each node autonomously decides whether the tasks are conditionally executed locally, or in networked neighboring nodes, or delegated to the Cloud based on the current nodes' context and statistical data relevance. We comprehensively evaluate our methodology demonstrating its applicability in edge computing environments

Constrainedness in stable matching

In constraint satisfaction problems, constrainedness provides a way to predict the number of solutions: for instances of a same size, the number of constraints is inversely correlated with the number of solutions. However, there is no obvious equivalent metric for stable matching problems. We introduce the contrarian score, a simple metric that is to matching problems what constrainedness is to constraint satisfaction problems. In addition to comparing the contrarian score against other potential tightness metrics, we test it for different instance sizes as well as extremely distinct versions of the stable matching problem. In all cases, we find that the correlation between contrarian score and number of solutions is very strong

A Framework for Plan Library Evolution in BDI Agent Systems

The Belief-Desire-Intention (BDI) paradigm is a flexible framework for representing intelligent agents. Practical BDI agent systems rely on a static plan library to reduce the planning problem to the simpler problem of plan selection. However, fixed pre-defined plan libraries are unable to adapt to fast-changing environments pervaded by uncertainty. In this paper, we advance the state-of-the-art in BDI agent systems by proposing a plan library evolution architecture with mechanisms to incorporate new plans (plan expansion) and drop old/unsuitable plans (plan contraction) to adapt to changes in a realistic environment. The proposal follows a principled approach to define plan library expansion and contraction operators, motivated by postulates that clearly highlight the underlying assumptions, and quantified by decision-support measures of temporal information. In particular, we demonstrate the feasibility of the proposed contraction operator by presenting a multi-criteria argumentation based decision making to remove plans exemplified in a planetary vehicle scenario

A Linked Data browser with recommendations

It is becoming more common to publish data in a way that accords with the Linked Data principles. In an effort to improve the human exploitation of this data, we propose a Linked Data browser that is enhanced with recommendation functionality. Based on a user profile, also represented as Linked Data, we propose a technique that we call LDRec that chooses in a personalized way which of the resources that lie within a certain neighbourhood in a Linked Data graph to recommend to the user. The recommendation technique, which is novel, is inspired by a collective classifier known as the Iterative Classification Algorithm. We evaluate LDRec using both an off-line experiment and a user trial. In the off-line experiment, we obtain higher hit rates than we obtain using a simpler classifier. In the user trial, comparing against the same simpler classifier, participants report significantly higher levels of overall satisfaction for LDRec

Sleep Activity Recognition using Binary Motion Sensors

International audienceEarly detection of frailty signs is important for the senior population that prefers to keep living in their homes instead of moving to a nursing home. Sleep quality is a good predictor for frailty monitoring. Thus we are interested in tracking sleep parameters like sleep wake patterns to predict and detect poten- tial sleep disturbances of the monitored senior res- idents. We use an unsupervised inference method based on actigraphy data generated by ambient mo- tion sensors scattered around the senior’s apartment. This enables our monitoring solution to be flexible and robust to the different types of housings it can equip while still attaining accuracy of 0.94 for sleep period estimates

Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development