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ef cient data aggregation in sensor networks is also proposed to be addressed by making use of location information [136] The basic idea behind data aggregation is to combine data generated by different sensor nodes If the location of the sensor node that generated the data is known, then data generated by sensor nodes positioned in the same locality can be naturally aggregated This works very well when the objective of the sensor network is to obtain information such as temperature and humidity of an area Other applications such as target tracking, environment monitoring, and several security mechanisms [137] also depend on location information In the case of examples such as target tracking and environment monitoring, the data might have to be accompanied by information about the location where the data was generated On the other hand security mechanisms to protect against wormhole attacks (see 4) require location information for their basic functioning Mechanisms to protect against attacks such as Sybil can also be buttressed using the location information to cross-check the other information In addition, there have recently also been proposals for location-based keying [67] The idea behind location-based keys is to tie the private key of a node to both its identity and geographic location This is opposed to tying the key just to the identity as done in conventional schemes The use of location information for authentication in addition to the use of identity has also been proposed [138] Thus, users who need access to the network need not only have the proper identity but also be in the proper location It is important to point out here that these location-based applications, services, and security mechanisms are sensitive to the accuracy of the location information For example, the authors in [139, 140] have investigated the effect of localization errors (due to accidental and not malicious reasons) on the performance of both GHT and a geographic MANET routing protocol GPSR They found that realistic localization errors can lead to incorrect behavior and signi cant performance degradation While it may be possible to modify the applications so that their performance is robust when dealing with statistically inaccurate information, it is a different matter when considering location information that is inaccurate due to the presence of an adversary This is due to the fact that it is not possible to factor the inaccuracy resulting from maliciousness In such a case, the gains afforded by the use of location information under normal conditions can very easily be erased It is also trivial to conclude that other applications such as data aggregation and target tracking will also suffer in the presence of maliciously modi ed location information Therefore it is necessary to investigate location determination techniques that have been designed with a focus on security This is more so considering that the type of networks that we are focusing on, namely ad hoc and sensor networks, naturally attract adversaries given their typical deployment scenarios The secure location determination techniques should prevent both malicious insider nodes from misrepresenting their location and outside entities from interfering with the location determination process being followed by the system components Thus the security mechanisms for the location determination schemes must be designed to be robust against two types of attackers These are the internal and the external attackers Internal attackers are those insider nodes that can authenticate themselves to the system and are hence part of the system Such nodes can report false location information or interfere with the localization process with the objective of cheating the location determination system External attackers are the outside entities that are not part of the system and hence do not know any system secrets The aim of such attackers is to convince an honest node or the location determination infrastructure or both that the node is present.



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Current speech recognition technology is at a rudimentary level; however, this is an area that is going to grow and evolve As it does, we envision that spoken responses (such as recordings of discussions, verbal presentations, or negotiations) will be incorporated into assessments and scored automatically Meanwhile, some progress has been made in creating and automatically scoring written responses (see eg, Martinez & Bennett, 1992) While it is easy to imagine these types of assessment, it is not clear at this point how to score them and obtain meaningful information from the resulting scores Natural language processing has been successfully applied to the scoring of written communications in several contexts It is reasonable to assume that these technologies will be extended to be able to replicate holistic human ratings assigned to even more complex texts However, being able to reproduce a score does not necessarily imply that we understand what that score means The typical portfolio assessment involves a small number of judges evaluating a unique set of work products It is not clear that these judges all use the same weighting of the different components of the portfolio Nor is it clear that the judges, faced with a different portfolio, would use the same weighting as was used for the rst one Both theoretical and practical developments will be required for our vision of unobtrusive assessment to become reality The intelligent agents that select the work will need to have the capability to assess the content of the work suf ciently to select a representative set of materials with the proper mix of content to meet the multi-dimensional pro le needed for the desired interpretation Substantively meaningful objective scoring methods will be required to produce accurate, standardized, and generalizable results Finally, we agree with van der Linden ( 2) that ancillary information such as time to complete a task, order of operations, and complexity of solution have great promise for richer interpretations of examinee work.

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Dec 28, 2018 · The following are full-fledged PDF editors: Adobe Acrobat Pro. Adobe Acrobat is the most popular PDF editor software, but there are many alternatives. Able2Extract Professional 14. AbleWord. Bluebeam Revu. ABBYY FineReader/FineReader Pro. Foxit PhantomPDF. Gaaiho PDF Suite 5. iSkySoft PDF Editor 6 Professional.












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