By Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera (auth.), Osmar R. Zaïane, Sandra Zilles (eds.)
This publication constitutes the refereed lawsuits of the twenty fifth Canadian convention on synthetic Intelligence, Canadian AI 2012, held in Regina, SK, Canada, in could 2013. The 17 average papers and 15 brief papers offered have been rigorously reviewed and chosen from seventy three preliminary submissions and are followed through eight papers from the Graduate pupil Symposium that have been chosen from 14 submissions. The papers disguise quite a few themes inside AI, akin to: info extraction, wisdom illustration, seek, textual content mining, social networks, temporal associations.
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Additional info for Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings
If the semantics of B ensure the ψ(X|Y ) = p(X|Y ), then ψ is denoted as pB (X|Y ); otherwise, it is denoted as φB (X|Y ). S is the set of variables marginalized in F (ψ). A(XS) and D(XS) are computed from the transitive closure, denoted T , of B . Algorithm 3. SI(ψ) Compute the evidence expanded form F (ψ) of ψ Compute the evidence normal form γ · N of F (ψ) Compute the CPT structure ψ(X|Y ) of N Compute Z = A(XS) ∩ D(XS) Compute X1 = X ∩ P (Z) if IB (X1 , ∅, Y ) holds in B by d-separation return pB (X|Y ) else return φB (X|Y ) Recall ψ(g, i = 1, j) in (2).
9) Koller and Friedman  also state it must necessarily be the case that p (b, d|c) = ψ(b, c, d), (10) where p (U ) is deﬁned by a diﬀerent Bayesian network B - the one given in Figure 2 (right). Our objective is to stipulate semantics in the current Bayesian network B - the one on which inference is being conducted. a a d b d b c c Fig. 2. Bayesian networks B (left) and B (right) Table 3. 5 CPT Structure It is instructive to review that, when evidence is not considered, each potential built by VE is a CPT.
A target patient has profiles on two online medical social networking sites. By comparing the attributes from both profiles, the adversary can link the two with high confidence. Furthermore, the attacker can use the attribute values to get more profiles of the target through searching the Web and other online public data sets. Medical information including lab test results was identified by aggregating and associating five profiles gathered by an attacker, including the patient’s full name, date of birth, spouse’s name, home address, home phone number, cell phone number, two email addresses, and occupation.
Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings by Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera (auth.), Osmar R. Zaïane, Sandra Zilles (eds.)