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-vim:ft=markdown
+# Miscelaneous
 
-= Changes
+For ACM, the title should not be the same as for the conference paper.
 
-== Multi-shot Classification
+
+
+# Explanation Additions 
+
+These are futher explanations of concepts that were only superficially presentedin the original paper.
+
+## Multi-shot Classification
 The system is designed to get the most out of one-shot training data. We explain in the discussion that there is a natural extension to multi-shot training data. However we don't provide much more explanation nor an evaluation of the performances.
 
--> Explain in details how the algorithm work with multiple training samples per class. Explain that this is a competitive system where samples of the same class compete against each other but play for the same team. There may be a change to make on the computation of the threshold that should only consider distances between different classes (min of distance between two samples of different classes, this has a specific name in hierarchical clustering design).
-Provide an evaluation in the case where a pattern can have multiple modes.
+* Explain in details how the algorithm work with multiple training samples per class. Explain that this is a competitive system where samples of the same class compete against each other but play for the same team. There may be a change to make on the computation of the threshold that should only consider distances between different classes (min of distance between two samples of different classes, this has a specific name in hierarchical clustering design).
+* Provide an evaluation in the case where a pattern can have multiple modes.
 
+## Time Efficiency & Parallelisation
+In the discussion we say that the time efficiency of MAD, while not being properly evaluated, is good enougth to not be prohibitive. However this is a very hand-waving argument and a proper evaluation would be more convincing. We also say that the algorithm can be naturally paralelized. This is true but we do not provide more explanation.
 
+* Do a proper time efficiency evaluation of the algorithm. This might be tricky but you took courses about that so figure it out. 
+* Provide a better explanation of the parallelisation of the algorithm. Provide a pseudo code on the same format as the main pseudo code in the paper, where the // sections are highlighted and explain. If // changes the time efficiency, provide a new evaluation for the // version.
+
+# Novel Additions
+
+These are additions that introduces completely new information that was no in the original paper.
+
+## New Experiment and AC current
+The main experiment of the QRS paper (Case Study 2) presented the detection of states for enforcing security rules.
+This was great but limited to only one machine with one set of states/rules.
+It would be interesting to add a new case study that illustrate the potential with other machines/scenarios.
+
+* Perform an experiment similar to Case Study 2 but focusing on AC current and non-intrusive measurement of power.
+ * Equip a tower PC with a power clamp on the AC line and perform the same kind of security policy enforcement.
+ * This might be tricky because all 8 clamps are in use for the LV experiment. Figure it out...
+* Perform a case study on a different device than a computer. It needs to be a machine with possible attacks and enougth different states to be interesting. Possible machines could be:
+ * WAP if there are more states than ON and OFF.
+ * Smart TV. Could identify getting into menues, playing of specific ports/peripherals, connection status etc. The policies would be related to usage state and not to attacks.