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V11-32

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Joined: 19 Mar 2012
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WE: Marketing (Non-Profit and Government)
V11-32  [#permalink]

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New post 04 Jan 2016, 11:47
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A
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C
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Difficulty:

  85% (hard)

Question Stats:

31% (01:15) correct 69% (01:09) wrong based on 36 sessions

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Systems employed during surveillance activities might be enhanced using anomaly detection capabilities. Such capabilities can be employed to highlight those situations, events or objects that need operator's attention, reducing, thus, their cognitive load and reaction time. Early detection of such situations provides critical time to take appropriate action with, possibly before potential problems occur. However, the detection of such conflict situations or general anomalous behavior in surveillance data is a complex analytical task that normally cannot be solved using purely visual analysis or purely automatic computational methods. On the one hand, the success of purely visual analysis methods for area surveillance often depend on factors such as the amount of sensor data that needs to be monitored, time constraints, or even operators' cognitive load and level of fatigue. On the other hand, current automatic anomaly detection solutions normally present high false alarm rates when dealing with complex situations. The high number of false alarms can become a nuisance for operators, who might react by turning anomaly detection capabilities off. Some researchers dispute the use of fully autonomous discovery systems in real-world settings, highlighting the need of including human knowledge in the discovery process. Most of the published work on anomaly detection focuses on the technological aspects: new and combinations of methods, additional improvements of existing methods, reduction of false alarms, correlations among alarms, etc. Publications regarding the use of anomaly detection methods in real environments, or human factors studies regarding anomaly detection, are scarce. In order to find optimal combinations of human expert knowledge and computational methods for anomaly detection, it is important to investigate how the surveillance of sea areas is carried out. This domain is suitable for the study of finding optimal combinations of expert knowledge and computational methods, since it fulfils the characteristics of many data-intensive domains – large amounts of multivariate data, the need for operator support to solve complex problems, the need for situation awareness to promote effective decision-making etc. Knowledge of how the analysis of traffic data is carried out in a daily-basis can be used to propose how to support such processes using data mining and visualization methods.

The passage does not necessarily imply any of the following statements except that

A. the main drawback of the autonomous anomaly discovery systems is that they can never achieve zero false alarm rates.
B. further research in sea traffic movement can determine the best possible combination of human expert knowledge and computational method.
C. research in surveillance system has not been properly carried out because of the difficulty of analysing huge amount of data associated with the domain.
D. some researchers do not disagree that fully automatic surveillance methods can practically eliminate the requirement of human intervention.
E. no research work has been done on the non-technological aspect of the anomaly detection system.

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Joined: 19 Mar 2012
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WE: Marketing (Non-Profit and Government)
Re V11-32  [#permalink]

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New post 04 Jan 2016, 11:48
Official Solution:

Systems employed during surveillance activities might be enhanced using anomaly detection capabilities. Such capabilities can be employed to highlight those situations, events or objects that need operator's attention, reducing, thus, their cognitive load and reaction time. Early detection of such situations provides critical time to take appropriate action with, possibly before potential problems occur. However, the detection of such conflict situations or general anomalous behavior in surveillance data is a complex analytical task that normally cannot be solved using purely visual analysis or purely automatic computational methods. On the one hand, the success of purely visual analysis methods for area surveillance often depend on factors such as the amount of sensor data that needs to be monitored, time constraints, or even operators' cognitive load and level of fatigue. On the other hand, current automatic anomaly detection solutions normally present high false alarm rates when dealing with complex situations. The high number of false alarms can become a nuisance for operators, who might react by turning anomaly detection capabilities off. Some researchers dispute the use of fully autonomous discovery systems in real-world settings, highlighting the need of including human knowledge in the discovery process. Most of the published work on anomaly detection focuses on the technological aspects: new and combinations of methods, additional improvements of existing methods, reduction of false alarms, correlations among alarms, etc. Publications regarding the use of anomaly detection methods in real environments, or human factors studies regarding anomaly detection, are scarce. In order to find optimal combinations of human expert knowledge and computational methods for anomaly detection, it is important to investigate how the surveillance of sea areas is carried out. This domain is suitable for the study of finding optimal combinations of expert knowledge and computational methods, since it fulfils the characteristics of many data-intensive domains – large amounts of multivariate data, the need for operator support to solve complex problems, the need for situation awareness to promote effective decision-making etc. Knowledge of how the analysis of traffic data is carried out in a daily-basis can be used to propose how to support such processes using data mining and visualization methods.

The passage does not necessarily imply any of the following statements except that

A. the main drawback of the autonomous anomaly discovery systems is that they can never achieve zero false alarm rates.
B. further research in sea traffic movement can determine the best possible combination of human expert knowledge and computational method.
C. research in surveillance system has not been properly carried out because of the difficulty of analysing huge amount of data associated with the domain.
D. some researchers do not disagree that fully automatic surveillance methods can practically eliminate the requirement of human intervention.
E. no research work has been done on the non-technological aspect of the anomaly detection system.

A. The drawback of the autonomous anomaly discovery systems is that they create high false alarms; this does not imply that they can never achieve zero false alarms. It could be possible that at least one system has achieved or will achieve zero false alarm condition. B. The passage does not clarify whether further research can or cannot determine the best possible combination; it just states the requirement of research. C. Difficulty in analysing high amount of data is not referred as the reason that research in surveillance system has not been carried out; on the contrary availability of high amount of data makes this domain suitable for research. D. The passage states some researchers dispute the use of fully autonomous discovery systems in real-world settings, implying that others do not dispute or disagree. E. Most of the works have been done in technological aspects does not imply that no work has been done on non-technological aspects.

Answer: D
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Re: V11-32  [#permalink]

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New post 01 Jun 2017, 00:54
What does it mean when the question sayd - "passage implies " ?
Is the information provided in the passage or the information has to be inferred from the context ?
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Re: V11-32  [#permalink]

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New post 18 Jun 2017, 00:39
The other researchers may not have an opinion , to infer that other researchers necessarily disagree with complete automation is logically inaccurate, The answer D here seems wrong. Please explain further.
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Re: V11-32  [#permalink]

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New post 05 Aug 2017, 01:39
hi , i have a confusion with option B .
The passage states . ''In order to find optimal combinations of human expert knowledge and computational methods for anomaly detection, it is important to investigate how the surveillance of sea areas is carried out ''

Thus why option B is wrong as it to states the same .
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Re V11-32  [#permalink]

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New post 24 Aug 2017, 20:50
I think this is a poor-quality question and the explanation isn't clear enough, please elaborate.
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Re: V11-32  [#permalink]

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New post 01 Apr 2018, 10:44
Doesn't some researchers mean anywhere from 1% to 100% of all researchers dispute the use of of fully autonomous discovery systems. So, the passage doesn't necessarily imply some do not "dispute or disagree" since it could be the case that 100% of all researchers "dispute the use of fully autonomous discovery systems in real-world settings".

P.s. some = "at-least 1 or 1% to 100%". Your explanation is implying some is defined as "not all", which is "not a 100% or 0% to 99%". Not all is the logical opposite of some.
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Re V11-32  [#permalink]

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New post 20 Oct 2018, 09:07
I think this is a poor-quality question and the explanation isn't clear enough, please elaborate. please explain why is choice B incorrect ?
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Re V11-32 &nbs [#permalink] 20 Oct 2018, 09:07
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