Conclusion and Future Directions Sample Clauses

Conclusion and Future Directions. Recent experimental work on DCD has continued to provide evidence of fundamental issues in motor control and learning in these children (e.g. internal modelling and MNS). At the same time, there has been growth in experimental work on constraints testing from an ecological perspective, postural control under different constraints, executive function and neuroimaging approaches. We have a clearer picture of the constellation of motor and cognitive deficits in DCD, but also critical examples of where specific neurocognitive hypotheses break down because they do not apply across all (even related) paradigms or task conditions. We propose here that a hybrid model of motor development and dysfunction can help recon- cile these various findings, accounting for individual differ- ences in performance, the clustering of deficits, variability as a function of task-environmental condition and change with maturation. A few pointers for future research are in order. No experimental study has yet tracked the dynamic interaction between parametric changes in task condition (viz con- straints), the dynamics of motor coordination and real-time functional brain activity, nor tracked over time the impact of training on these factors. Conducted longitudinally, this type of study will advance knowledge of causal processes in DCD; embedded within a hybrid (multi-component) model, findings of this type will have important implications for intervention [62, 63]. Great advances have been made in the study of motor con- trol issues that are associated with DCD and their parallels with CP. Given these converging lines of evidence, there is a compelling case to merge approaches to the study of develop- mental motor disorders (aka hybrid model), the endgame be- ing to fully understand the interplay between individual, task and environmental constraints in their expression. Put another way, a hybrid model is an important next step in explaining the vast variability in the manifestation of motor control issues in CP and DCD and may further provide insight in the common- alities between DCD and CP, even suggesting that these dis- orders may not be distinct, but rather expressed on a continu- um. There is, indeed, a clinical viewpoint that supports this approach.45 Longitudinal studies are needed to map develop- mental trajectories in these groups and to understand whether parallel growth exists on key measures. Compliance with Ethical Standards Conflict of Interest The authors declare t...
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Conclusion and Future Directions. As envisaged under the project proposal, Deliverable 8.2 has been able to map the model IP agreements for pre-competitive access to microbial research data generated in marine bioprospecting missions and also to propose legally valid provisions concerning data for the proposed model ABS Agreement developed under Work Package 8. 1. The next two steps in the project are the development of the data policy guidelines and the fine tuning of the conditions in the model ABS Agreement with the help of the science community, so as to move towards the implementation of the model agreements for pre-competitive access to microbial genomic research databases. The latter will be done by conducting semi-structured interviews amongst the main business and science stakeholders. Such an effort would help to map the terms and conditions of the actual exchange practices. This would also help to identify the costs and benefits of the various model contracts for the stakeholders and society at large. A multi-stakeholder expert workshop will also be organized later to validate the 100 See Art. 6.3 of the model ABS Agreement. preliminary report of the survey and to build consensus on the contents of the proposed model agreements. NERC has a policy on data in order to: a. Ensure the continuing availability of environmental data of long-term value for research, teaching, and for wider exploitation for the public good, by individuals, government, business and other organisations. b. Support the integrity, transparency and openness of the research it supports. c. Help in the formal publication of data sets, as well as enabling the tracking of their usage to be tracked through citation and data licences. d. Meet relevant legislation and government guidance on the management and distribution of environmental information. NERC defines environmental data as individual items or records (both digital and analogue) usually obtained by measurement, observation or modelling of the natural world and the impact of humans upon it. This includes data generated through complex systems, such as information retrieval algorithms, data assimilation techniques and the application of models. This policy covers environmental data acquired, assembled or created through research, survey and monitoring activities that are either fully or partially funded by NERC. It also applies to environmental data managed by NERC where NERC was not the original funder. This policy does not cover NERC's information produc...
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Conclusion and Future Directions. In this chapter we demonstrated the applicability of frequent itemset mining in the anal- ysis of criminal characteristics for strategic purposes. The tool we described compiled a list of noteworthy relations between crime types and most important demographic char- acteristics. The nature of a criminal record database established the need for specifically suited adaptations of standard mining algorithms to cope with over- and under-presence of and inherit relations between attributes. The end report consists of a visual, scalable and clickable version of the resulting trie and is ready to be used by police experts. The semantic split proposed in this chapter already exploits the semantic knowledge of the analyst using the system. This can be extended to a more detailed level, a semantic bond, where semantic overlaps between two or more attributes can be defined. Charac- teristics in such a set should then not be combined in the detection phase. This way the coarse semantic split can be updated to a finer level of semantic coherence. For this research, the search for relations was focused on positive relations, meaning, that two or more attributes appear notably often together. It may also be of interest to the law enforcer to search for attributes that appear reasonably seldom together. However, the search for those relations with our method is hindered by the boolean nature of the database, required by standard approaches, and the way we aggregate those from the original nominal or numerical attributes: aggregated attributes never appear together by definition. One way to solve this might be to join them into a semantic bond as mentioned above. Other possibilities might also be applicable. Future research will aim at improving on the concerns mentioned above. After these issues have been properly addressed, research will mainly focus on the automatic com- parison between the results provided by our tool and the results social studies reached on the same subject, in the hope that “the best of both worlds” will reach even better analyzing possibilities for the police experts in the field. Incorporation of this tool in a data mining framework for automatic police analysis of their data sources is also a future topic of interest.
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Conclusion and Future Directions. Our experiments show that Object-Centered Interactive MDS has potential. It can be used for direct manipulation of clustering results based on a heuristic MDS approximation. It can help in verifying the MDS result, and help to generate hypotheses about alternative object relations, that were not found, for example, because the MDS converged to a local optimum. However, currently its usefulness is somewhat limited on highly unstructured data. Future work includes adding multiple-object drag-and-drop, extensive user testing, and scaling mechanisms like those introduced by Xxxxxxxx and Xxxxxxx [35]. Also, re- lating the re-placement of single items with a high local error (a high contribution to the global error) to the change in the global error is important for the analysis of the proposed approach. Changes in the global error can be represented by a Boolean figure (higher or lower) or be represented by a (color-)scale during the process of human intervention. Future research can strive to find a relation between the decrease in local errors and the error made in the total image. If such a positive relation exists, automating the process of relocating those items with the highest local error can be an asset worth pursuing. This global optimization can be of interest in areas where the correctness of the image as a whole is more important than the relation between a small subset of individual items, like, for example, a clustering on customer behavior. Our main goal is to use the proposed tool as part of a larger data mining framework for law enforcement purposes as described in Chapters 4 and 5. These chapters focus on the contributions a domain expert can make to the process of analyzing criminal careers using our interactive clustering tool.
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Conclusion and Future Directions. In this chapter we demonstrated the applicability of data mining in the field of xxxxx- nal career analysis. The tool we described compiled a criminal profile out of the four
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Conclusion and Future Directions. In this chapter we demonstrated the applicability of some newly developed methods in the field of automated criminal career analysis. Each of the enhancements provided a sig- nificant gain in computational complexity or improved the analysis on a semantic level. An integral part of the new setup consists of the multiset metric that was specifically tuned toward the comparison of criminal activities. It leaned strongly upon knowledge provided by domain experts. This distance measure was used to search for optimal align- ments between criminal careers. This edit distance for sorted or temporal multiset data improved greatly upon the original year-by-year comparison, dealing with problematic situations like captivity or forced inactivity. Using this alignment a distance matrix can be constructed and clustered on a flat surface using a new unsupervised method, yielding comparable results with the previous setup, but largely reducing the time complexity. The power of this visual representation was employed to extrapolate a starting career within the plane of the visualization, predicting its unfolding with some accuracy. The end re- port consists of a visual two-dimensional visualization of the results and a prototype of an early warning system that predicts newly developing criminal careers, which is ready to be used by police experts. This chapter describes improvements on a methodology for the analysis of criminal careers introduced in Chapter 4. Building upon a series of successfully introduced algo- rithms, this novel approach is subject to a high number of parameters and leans heavily on the way these techniques work together. It is to be expected that the current setup can also be improved by tweaking its parameters and elaborating on the internal cooperation between phases. Future work will therefore focus on a larger experimental setup to find and verify optimal settings and retrieve results that are even more descriptive and usable. We also hope to equip our tool with a sub-cluster detection algorithm to provide even better insights into the comparability of criminal careers. It may be of interest to set fuzzy borders between the different years. Crimes within months ending or beginning such a time frame can be (partly) assigned to the next or previous year respectively as well, thus eliminating the problems arising with strict co- herence to the change of calendar year. Special attention will be directed toward the predictability of criminal careers,...
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Conclusion and Future Directions. In this chapter we described a method that discovers common subcareers from a criminal record database. For this purpose we adapted a well-known algorithm that was more suit- able for the retail industry, modifying a number of different phases to suit the search for these frequently occurring subcareers. A representation of the database was chosen that was computationally hard to create but allowed fast searching in the next phase, which led to a better performance overall. Within this representation, we introduced the notion of escape edges that made it easy to find certain types of subcareers that would otherwise have been located very slowly. We also presented a number of results, investigating both 15 10 5 0 Youth White collar Drugs Theft (minor Other minor Violence Organized Other heavy Vice Unclassified career criminals) career criminals career criminals
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Conclusion and Future Directions. In this chapter we demonstrated the applicability of temporal extrapolation by using the prefabricated visualization of a clustering of reference items. This method assumes that the visualization of a clustering inherently contains a certain truth value that can yield
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