Support Set Research Articles

Expectation Maximization Indoor Localization Utilizing Supporting Set for Internet of Things

With the growth of WLAN infrastructure, received signal strength (RSS) fingerprint-based WiFi indoor positioning systems have received considerable attention recently. Some existing RSS fingerprint-based localization methods estimate locations by directly matching an online testing sample with an offline database, and thus show low accuracy because RSS is known to be vulnerable to variations caused by changing environment and heterogeneous hardware. To overcome the above drawbacks, we propose an expectation maximization indoor localization approach by leveraging supporting set (EMSS). In the offline phase, we first divide a positioning area into ${G}$ grid points and index each grid by a label. All the indices of grid points form a label set $ {\Psi =\{1,2,\ldots, G\}}$ . Then, we collect the RSS fingerprints to construct an offline database for all labeled grid points. In the online phase, given an online RSS testing sample, we first construct a supporting set (SS), which is a subset of $ {\Psi }$ , selected by the similarity between the online RSS sample and offline database. So, SS is a latent space that likely includes the true label (location) of the user. Based on the SS, we then derive an expectation maximization (EM) algorithm by incorporating the fingerprint quality into the estimation of the true label. EM can intelligently estimate the location of the user by evaluating the fingerprint quality of SS. Furthermore, we propose an optimal size selection algorithm based on Bayesian information criterion to adaptively determine the size of SS. Our method can effectively mitigate the impacts of changing environment and heterogeneous hardware without fingerprint and hardware calibrations, and can thus be practically applied. Experimental results verify that EMSS performs significantly better than some existing fingerprint-based methods.

Unintended effects in settings-based health promotion.

The settings-based approach to health promotion (HP) employs a social ecological (SE) framework to integrate HP into the usual activities of the setting and to increase the setting's support for healthy choices. The SE approach calls for systems thinking to account for the inextricable relationship between people, their behaviour and their environment. Knowledge about a setting can be used to mobilise people to participate in HP, to optimise success by taking into account the local context, and to anticipate and avoid barriers to success. In other words, the SE approach aims to help HP reach its goals for better health, established in concert with community needs and wishes. Yet, the focus on HP goals may detract attention from how intervention may have unanticipated, and even untoward effects on the setting. There is much evidence from classical ecological research that well-meaning interventions have unintended effects. Biology is so tuned to the possibility that the study of unintended effects is integral to the field. There is some evidence--but much less--that HP also has unexpected, deleterious effects. The evidence is limited because of neglect; the subject of unintended effects is only of peripheral interest in HP. This is a call for a more robust SE approach, in which frameworks used to guide settings-based HP are augmented so as to be concerned with planned effects, and also unplanned effects. What can be done to more responsibly monitor, document and report the full panoply of our effects, including detecting and preventing untoward effects?

Using layered support graphs for verifying external adequacy in rule-based expert systems

The problem of verifying external adequacy in expert systems is addressed in this paper. External adequacy is defined in terms of a formal specification of question relevance, that expresses natural data dependencies among the external facts used in the ontology of the domain of expertise. A method is presented for verifying the expert system, at any stage of development, against the formal specification. The method is based on transforming the knowledge base plus inference engine into a partially ordered set of support graphs. The complex problem of verifying the external adequacy of expert systems is then translated into a simpler path-finding problem in support graphs.