Databases hand labelling

The use of automatic labelling algorithms is normally justified in terms of saving time [384], [5], [279]. In the past, we had small databases which could be labelled by hand, but as the databases we use today are larger, and because sometimes we wish to label the database very quickly, we require an automatic system. This argument basically says that automatic labelling is therefore a matter of convenience if we had an infinite supply of hand labellers we wouldn t have a problem. A second justification is that automatic systems can out perform human labellers in terms of accuracy and consistency, and so convenience alone is not the only justification automatic systems are in fact better, in our experience this certainly appears to be true. 

Most people assume all X7Rs are the same. They actually think it is a specific material, and that all vendors with an X7R capacitor on hand are equivalent competitors. That is simply not true. Even a given vendor can have several X7R formulations with dielectric constants ranging from 1000 to 7000. X7R only refers to a material with a TCC of 15% over -55°C to 125°C. And that too, only with zero applied volts (or close to it). Take a look at Figure 4-6. These are curves extracted and superimposed (rather painfully) from the Epcos database of MLCCs. You can clearly see that all these are labeled X7R, but their temperature profile visibly falls into two main categories. So, if somebody says to you the... 

If the principal barrier to switch has been a lack of clinical experience with a drug, this can be remedied by the collection of a large adverse reaction database. Once this is done, it is usually straightforward to establish that the drug is safe in prescription use. This is a major advance on the road to OTC approval, but it certainly does not yet prove that the drug will be safe and effective in the hands of consumers without the benefit of a learned intermediary. In order to establish this additional point, it is almost always necessary to supplement the analysis of adverse reaction databases with clinical studies in realistic conditions, using the labeling composed for the OTC product. 

Finally, using a linear phonemic representation has the benefit that it makes automatic database labelling considerably easier. In Chapter 17 we shall consider the issue of labelling a speech database with phoneme units, both by hand and by computer. As we shall see, it is easier to perform this labelling if we assume a linear pronunciation model, as this works well with automatic techniques such as hidden Markov models. 

Two major problems stem from this. Firstly, any database which has been labelled with ToBI will have a significant amount of noise associated with the pitch accent label classes. Secondly, for any large scale machine learning or data driven approach, we need a considerable amount of labelled data to the extent that it is impractical to label data by hand. As we shall see in Chapters 15 and 16, virtually all other aspects of a modem data driven TTS system s data are labelled automatically, and so it is a significant drawback if the intonation component can not be labelled automatically as well. Because however the level of human labeller agreement is so low, it is very hard to train a system successfully on these labels we can hardly expect an automatic algorithm to perform better than a human at such a task. 

Hazardous material (HAZMAT)-. Effective HMIS (hazardous material identification system) and MSDS (material safety data sheet) programs are both essential elements of a safety program The HMIS system identifies and labels substances that may pose some hazard to those who use them. The MSD sheets, provided by the suppliers of materials deemed to be hazardous, are kept on file to quickly identify all hazards and the appropriate responses in emergency situations. This mandated part of a safety program can be handed off to a subteam that ensures the MSDS database in updated and the HMIS labeling program is audited for compliance. 

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