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V11-34

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MBA Section Director
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P
Joined: 19 Mar 2012
Posts: 4480

Kudos [?]: 17007 [0], given: 1962

Location: India
GMAT 1: 760 Q50 V42
GPA: 3.8
WE: Marketing (Non-Profit and Government)
V11-34 [#permalink]

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New post 04 Jan 2016, 11:52
00:00
A
B
C
D
E

Difficulty:

  55% (hard)

Question Stats:

9% (00:50) correct 91% (01:50) wrong based on 11 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.

Which of the following statements is contradicted by the implications presented in the passage?

A. Some optimisation problems require a lot of statistics and facts to be available in order to be considered a potential research field.
B. Operators are not authorised to change the settings of the automatic anomaly detection systems.
C. The automatic anomaly detection systems need to be more sensitive in order to be more effective in complex situations.
D. To correctly choose a course of action, operators of maritime surveillance system need perception of prevailing conditions.
E. Early detection of anomalous situation cannot always help avoid mishaps.
[Reveal] Spoiler: OA

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Kudos [?]: 17007 [0], given: 1962

Expert Post
MBA Section Director
User avatar
P
Joined: 19 Mar 2012
Posts: 4480

Kudos [?]: 17007 [0], given: 1962

Location: India
GMAT 1: 760 Q50 V42
GPA: 3.8
WE: Marketing (Non-Profit and Government)
Re V11-34 [#permalink]

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New post 04 Jan 2016, 11:52
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.

Which of the following statements is contradicted by the implications presented in the passage?

A. Some optimisation problems require a lot of statistics and facts to be available in order to be considered a potential research field.
B. Operators are not authorised to change the settings of the automatic anomaly detection systems.
C. The automatic anomaly detection systems need to be more sensitive in order to be more effective in complex situations.
D. To correctly choose a course of action, operators of maritime surveillance system need perception of prevailing conditions.
E. Early detection of anomalous situation cannot always help avoid mishaps.

A. This option is directly supported in the passage: 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. B. Operators change the settings when they receive high false alarms. However this does not imply that they are authorised to do so. C. As per the passage, the automatic detection systems are problematic because they create high number of false alarms, and increasing the sensitivity of these systems would further increase the number of false alarms, making this system even more problematic. Option C therefore contradicts what is implied by the passage. D. This statement is directly supported in the passage: the need for situation awareness to promote effective decision-making. E. Early detection of anomalous situations provides critical time to take appropriate action with, possibly before potential problems occur. This statement implies that not always it is possible to avoid problems. Hence option E is supported by the passage.

Answer: C
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Kudos [?]: 17007 [0], given: 1962

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Joined: 05 Nov 2015
Posts: 43

Kudos [?]: 10 [0], given: 21

Re: V11-34 [#permalink]

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New post 29 Mar 2016, 01:14
I agree with C as the correct answer, But IMO you can't rule out B either. In matters of surveillance i don't think the operators would turn the system off unless they have the authority to do so. It might be an isolated case but the author wouldn't certainly attribute that as a distinct possibility.

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B
Joined: 12 Feb 2015
Posts: 32

Kudos [?]: [0], given: 259

Location: India
Schools: HEC Montreal '20
GMAT 1: 610 Q47 V28
GPA: 3.84
CAT Tests
V11-34 [#permalink]

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New post 18 Aug 2017, 22:16
souvik101990 wrote:
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.

Which of the following statements is contradicted by the implications presented in the passage?

A. Some optimisation problems require a lot of statistics and facts to be available in order to be considered a potential research field.
B. Operators are not authorised to change the settings of the automatic anomaly detection systems.
C. The automatic anomaly detection systems need to be more sensitive in order to be more effective in complex situations.
D. To correctly choose a course of action, operators of maritime surveillance system need perception of prevailing conditions.
E. Early detection of anomalous situation cannot always help avoid mishaps.

A. This option is directly supported in the passage: 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. B. Operators change the settings when they receive high false alarms. However this does not imply that they are authorised to do so. C. As per the passage, the automatic detection systems are problematic because they create high number of false alarms, and increasing the sensitivity of these systems would further increase the number of false alarms, making this system even more problematic. Option C therefore contradicts what is implied by the passage. D. This statement is directly supported in the passage: the need for situation awareness to promote effective decision-making. E. Early detection of anomalous situations provides critical time to take appropriate action with, possibly before potential problems occur. This statement implies that not always it is possible to avoid problems. Hence option E is supported by the passage.

Answer: C


Operators change the settings when they receive high false alarms. However this does not imply that they are authorised to do so,and in data intensive domain-the need for operator support to solve complex problems-the operators are required to do something implies that they are authorized to do s when the problems are complex and automatic systems includes complex problems

If this is so then why we are ruling out B option.

Kudos [?]: [0], given: 259

V11-34   [#permalink] 18 Aug 2017, 22:16
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