An intelligent Geographic Information System for design

Recent advances in geographic information systems (GIS) and artificial intelligence (AI) techniques have been summarised, concentrating on the theoretical aspects of their construction and use. Existing projects combining AI and GIS have also been discussed, with attention paid to the interfacing methods used and problems uncovered by the approaches. AI and GIS have been combined in this research to create an intelligent GIS for design. This has been applied to off-shore pipeline route design. The system was tested using data from a real pipeline design project. [Continues.

Analogical reasoning in uncovering the meaning of digital-technology terms: the case of backdoor

[EN] The paper substantiates the critical role of analogical reasoning and figurative languge in resolving the ambiguity of cybersecurity terms in various expert communities. Dwelling on the divergent interpretations of a backdoor, it uncovers the potential of metaphor to serve both as an interpretative mechanism and as a framing tool in the ongoing digital technologies discourse. By combining methods of corpus research and frame semantics analysis the study examines the challenges of unpacking the meaning of the contested concept of the backdoor. The paper proposes a qualitatively new metaphor-facilitated mode of interpreting cybersecurity vulnerabilities based on MetaNet deep semantic metaphor analysis and outlines the merits of this hierarchically organized metaphor and frames ontology. The utility of the method is demonstrated through analyzing corpus data and top-down extracting of metaphors (linguistic metaphor – conceptual metaphor – entailed metaphor – inferences) with subsequent identifying of metaphor families dominating the cybersecurity discourse. The paper further claims that the predominant metaphors prompt certain decisions and solutions affecting information security policies. Skrynnikova, IV. (2020). Analogical reasoning in uncovering the meaning of digital-technology terms: the case of backdoor. Journal of Computer-Assisted Linguistic Research. 4(1):23-46. https://doi.org/10.4995/jclr.2020.12921OJS234641Betz, David and Stevens, Tim. 2013. "Analogical Reasoning and Cyber Security." Security Dialogue 44, No. 2: 147-164 (2013). https://doi.org/10.1177/0967010613478323David, Oana and Matlock, Teenie. 2018. "Cross-linguistic automated detection of metaphors for poverty and cancer." Language and Cognition 10 (2018), 467-493. UK Cognitive Linguistics Association. https://doi.org/10.1017/langcog.2018.11David, Oana. 2016. Metaphor in the grammar of argument realization. Unpublished doctoral dissertation, University of California, Berkeley.David, Oana, Lakoff, George, and Stickles, Elise. 2016. "Cascades in metaphor and grammar: A case study of metaphors in the gun debate." Constructions and Frames. 8. 10.1075/cf.8.2.04dav. https://doi.org/10.1075/cf.8.2.04davDavies, Mark. 2013. "Corpus of Global Web-Based English: 1.9 billion words from speakers in 20 countries." Available at: http://corpus.byu.edu/glowbe/Davies, Mark. and Fuchs, Robert. 2015. "Expanding horizons in the study of World Englishes with the 1.9 billion word Global Web-based English Corpus (GloWbE)." English World-Wide 36(1), 1-28. https://doi.org/10.1075/eww.36.1.01davDeignan, Alice. 2005. Metaphor and corpus linguistics. Amsterdam/Philadelphia: John Benjamins. https://doi.org/10.1075/celcr.6Demjén, Zsófia, Semino, Elena, and Koller, Veronika. 2016. "Metaphors for 'good' and 'bad' deaths." Metaphor and the Social World 6(1), 1-19. https://doi.org/10.1075/msw.6.1.01demDodge, Ellen. K., Hong, Jisup, and Stickles, Elise. 2015. "MetaNet: deep semantic automatic metaphor analysis." Proceedings of the Third Workshop on Metaphor in NLP, 40-49. Denver, Colorado, 5 June 2015. Association for Computational Linguistics. https://doi.org/10.3115/v1/W15-1405Do Dinh, Erik-Lân and Gurevych, Iryna. 2016. "Token-level metaphor detection using neural networks." Proceedings of the Fourth Workshop on Metaphor in NLP (June), 28-33. https://doi.org/10.18653/v1/W16-1104Dunn, Jonathan. 2013. "What metaphor identification systems can tell us about metaphor-inlanguage." Proceedings of the First Workshop on Metaphor in NLP, Atlanta Georgia, 13 June 2010, 1-10. Available at: http://www.aclweb.org/anthology/W13-0901Fillmore, Charles J. and Atkins, Beryl. T. 1992. "Toward a frame-based lexicon: the semantics of RISK and its neighbors." In Frames, fields, and contrasts: new essays in semantic and lexical organization, edited by A. Lehrer and E. F. Kittay, 75-102. New York/London: Routledge.Gedigian, M., Bryant, J., Narayanan, S., and Ciric, B. 2006. "Catching metaphors." Proceedings of the Third Workshop on Scalable Natural Language Understanding ScaNaLU 06 (June), 41-48. https://doi.org/10.3115/1621459.1621467Gill, Lex. 2018. "Law, Metaphor, and the Encrypted Machine." Osgoode Hall Law Journal 55.2: 440-477. Available at: https://digitalcommons.osgoode.yorku.ca/ohlj/vol55/iss2/3Gutiérrez, E. Dario, Shutova, Ekaterina, Marghetis, Tyler, and Bergen Benjamin. 2016. "Literal and metaphorical senses in compositional distributional semantic models." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, August 7-12, 2016, 183-193. https://doi.org/10.18653/v1/P16-1018Hallam-Baker, Phillip. 2008. dotCrime Manifesto: How to Stop Internet Crime. Addison-Wesley.Jenner, Leontine. 2018. "Backdoor: how a metaphor turns into a weapon." Available at: https://www.hiig.de/en/backdoor-how-a-metaphor-turns-into-a-weapon/Krishnakumaran, Saisuresh and Zhu, Xiaojin. 2007. "Hunting elusive metaphors using lexical resources." In Proceedings of the Workshop on Computational Approaches to Figurative Language, 13-20. Association for Computational Linguistics. https://doi.org/10.3115/1611528.1611531Kupers, Wendelin M. 2013. "Embodied transformative metaphors and narratives in organisational life‐worlds of change." Journal of Organizational Change Management, Vol. 26 Issue: 3, 494-528. https://doi.org/10.1108/09534811311328551Lakoff, George. 1993. "The contemporary theory of metaphor". In Metaphor and thought, edited by A. Ortony, 202-251. New York, NY, US: Cambridge University Press. https://doi.org/10.1017/CBO9781139173865.013Lakoff, George, and Johnson, Mark. 1980. Metaphors we live by. Chicago, IL: University of Chicago Press.Landwehr, C., Bull, A. R., McDermott, J. P., and Choi, W. S. 1994. "A Taxonomy of Computer Program Security Flaws, with Examples." ACM Computing Surv., vol. 26, no. 3, 211-254. https://doi.org/10.1145/185403.185412Lederer, Jenny. (2013). "Assessing claims of metaphorical salience through corpus data." In Proceedings of the 37th Annual Meeting of the Cognitive Science Society, editored by D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings and P. P. Maglio, 1255-1260. Austin, TX: Cognitive Science Society.Lönneker, Birte. 2003. "Is there a way to represent metaphors in WordNets? Insights from the Hamburg Metaphor Database." Proceedings of the ACL 2003 Workshop on Lexicon and Figurative Language - Volume 14, 18-27. https://doi.org/10.3115/1118975.1118978Martin, James H. 2006. "A corpus-based analysis of context effects on metaphor comprehension." In Corpus-based approaches to metaphor and metonymy edited by S. T. Gries and A. Stefanowitsch, 214-236. Berlin: Mouton de Gruyter.Martin, James H. 1994. "MetaBank: a knowledge-base of metaphoric language conventions." Computational Intelligence 10(2), 134-149. https://doi.org/10.1111/j.1467-8640.1994.tb00161.xMason, Z. J. 2004. "CorMet: a computational, corpus-based conventional metaphor extraction system." Computational Linguistics 30(1), 23-44.https://doi.org/10.1162/089120104773633376Philip, G. 2004. "Locating metaphor candidates in specialized corpora using raw frequency and keyword lists." In Metaphor in use: context, culture, and communication edited by F. MacArthur, J. L. Oncins-Martínez, M. Sánchez-García and A. M. Piquer-Píriz, 85-105.Amsterdam: John Benjamins.Pragglejaz Group. 2007. "MIP: a method for identifying metaphorically used words in discourse." Metaphor and Symbol 22(1), 1-39. https://doi.org/10.1080/10926480709336752Shutova, Ekaterina, Teufel, Simone, and Korhonen, Anna. 2012. "Statistical metaphor processing." Computational Linguistics 39(2), 301-353. https://doi.org/10.1162/COLI_a_00124Shutova, Ekaterina and Sun, Lin. 2013. "Unsupervised metaphor identification using hierarchical graph factorization clustering." In Proceedings of NAACL-HLT 2013, Atlanta, Georgia, 9-14 June 2013, 978-988. Available at: http://www.aclweb.org/anthology/N13-1118Skrynnikova, Inna, Astafurova, Tatiana, and Sytina, Nadezhda. 2017. "Power of metaphor: cultural narratives in political persuasion." Proceedings of the 7th International Scientific and Practical Conference "Current issues of linguistics and didactics: The interdisciplinary approach in humanities" (CILDIAH 2017). https://doi.org/10.2991/cildiah-17.2017.50Steen, Gerard J., Dorst, Aletta, Berenike, Herrmann J., Kaal, Anna A., Krennmayr, Tina, and Pasma, Trijntje. 2010. A method for linguistic metaphor identification: from MIP to MIPVU. Amsterdam: John Benjamins. https://doi.org/10.1075/celcr.14Steen, Gerard, J. 1999. "From linguistic to conceptual metaphor in five steps." In Metaphor in cognitive linguistics, edited by R. W. Gibbs and G. J. Steen (Eds.), 57-77. Amsterdam/Philadelphia: John Benjamins. https://doi.org/10.1075/cilt.175.05steStefanowitsch, Anatol, and Gries, Stefan Th., eds. 2006. Corpus based approaches to metaphor and metonymy. Berlin/New York: Mouton de Gruyter. https://doi.org/10.1515/9783110199895Stickles, Elise, David, Oana, Dodge, Ellen K., and Hong, Jisup. 2016. "Formalizing contemporary conceptual metaphor theory." Constructions and Frames 8(2), 166-213. https://doi.org/10.1075/cf.8.2.03stiWolff, Josephine. 2014. "Cybersecurity as Metaphor: Policy and Defense Implications of Computer Security Metaphors." Paper presented at TPRC Conference, March 31, 2014. https://doi.org/10.2139/ssrn.241863

Adaptive User Interfaces for Intelligent E-Learning: Issues and Trends

Adaptive User Interfaces have a long history rooted in the emergence of such eminent technologies as Artificial Intelligence, Soft Computing, Graphical User Interface, JAVA, Internet, and Mobile Services. More specifically, the advent and advancement of the Web and Mobile Learning Services has brought forward adaptivity as an immensely important issue for both efficacy and acceptability of such services. The success of such a learning process depends on the intelligent context-oriented presentation of the domain knowledge and its adaptivity in terms of complexity and granularity consistent to the learner’s cognitive level/progress. Researchers have always deemed adaptive user interfaces as a promising solution in this regard. However, the richness in the human behavior, technological opportunities, and contextual nature of information offers daunting challenges. These require creativity, cross-domain synergy, cross-cultural and cross-demographic understanding, and an adequate representation of mission and conception of the task. This paper provides a review of state-of-the-art in adaptive user interface research in Intelligent Multimedia Educational Systems and related areas with an emphasis on core issues and future directions

Fifth Conference on Artificial Intelligence for Space Applications

The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration

Natural language processing and advanced information management

Integrating diverse information sources and application software in a principled and general manner will require a very capable advanced information management (AIM) system. In particular, such a system will need a comprehensive addressing scheme to locate the material in its docuverse. It will also need a natural language processing (NLP) system of great sophistication. It seems that the NLP system must serve three functions. First, it provides an natural language interface (NLI) for the users. Second, it serves as the core component that understands and makes use of the real-world interpretations (RWIs) contained in the docuverse. Third, it enables the reasoning specialists (RSs) to arrive at conclusions that can be transformed into procedures that will satisfy the users' requests. The best candidate for an intelligent agent that can satisfactorily make use of RSs and transform documents (TDs) appears to be an object oriented data base (OODB). OODBs have, apparently, an inherent capacity to use the large numbers of RSs and TDs that will be required by an AIM system and an inherent capacity to use them in an effective way

THE DEVELOPMENT OF A HOLISTIC EXPERT SYSTEM FOR INTEGRATED COASTAL ZONE MANAGEMENT

Coastal data and information comprise a massive and complex resource, which is vital to the practice of Integrated Coastal Zone Management (ICZM), an increasingly important application. ICZM is just as complex, but uses the holistic paradigm to deal with the sophistication. The application domain and its resource require a tool of matching characteristics, which is facilitated by the current wide availability of high performance computing. An object-oriented expert system, COAMES, has been constructed to prove this concept. The application of expert systems to ICZM in particular has been flagged as a viable challenge and yet very few have taken it up. COAMES uses the Dempster- Shafer theory of evidence to reason with uncertainty and importantly introduces the power of ignorance and integration to model the holistic approach. In addition, object orientation enables a modular approach, embodied in the inference engine - knowledge base separation. Two case studies have been developed to test COAMES. In both case studies, knowledge has been successfully used to drive data and actions using metadata. Thus a holism of data, information and knowledge has been achieved. Also, a technological holism has been proved through the effective classification of landforms on the rapidly eroding Holderness coast. A holism across disciplines and CZM institutions has been effected by intelligent metadata management of a Fal Estuary dataset. Finally, the differing spatial and temporal scales that the two case studies operate at implicitly demonstrate a holism of scale, though explicit means of managing scale were suggested. In all cases the same knowledge structure was used to effectively manage and disseminate coastal data, information and knowledge

Knowledge-based automatic tolerance analysis system

Tolerance measure is an important part of engineering, however, to date the system of applying this important technology has been left to the assessment of the engineer using appropriate guidelines. This work offers a major departure from the trial and error or random number generation techniques that have been used previously by using a knowledge-based system to ensure the intelligent optimisation within the manufacturing system. A system to optimise manufacturing tolerance allocation to a part known as Knowledge-based Automatic Tolerance Analysis (KATA) has been developed. KATA is a knowledge-based system shell built within AutoCAD. It has the ability for geometry creation in CAD and the capability to optimise the tolerance heuristically as an expert system. Besides the worst-case tolerancing equation to optimise the tolerance allocation, KATA's algorithm is supported by actual production information such as machine capability, types of cutting tools, materials, process capabilities etc. KATA's prototype is currently able to analyse a cylindrical shape workpiece and a simple prismatic part. Analyses of tolerance include dimensional tolerance and geometrical tolerance. KATA is also able to do angular cuts such as tapers and chamfers. The investigation has also led to the significant development of the single tolerance reference technique. This method departs from the common practice of multiple tolerance referencing technique to optimise tolerance allocation. Utilisation of this new technique has eradicated the error of tolerance stackup. The retests have been undertaken, two of which are cylindrical parts meant to test dimensional tolerance and an angular cut. The third is a simple prismatic part to experiment with the geometrical tolerance analysis. The ability to optimise tolerance allocation is based on real production data and not imaginary or random number generation and has improved the accuracy of the expected result after manufacturing. Any failure caused by machining parameters is cautioned at an early stage before an actual production run has commenced. Thus, the manufacturer is assured that the product manufactured will be within the required tolerance limits. Being the central database for all production capability information enables KATA to opt for several approaches and techniques of processing. Hence, giving the user flexibility of selecting the process plan best suited for any required situation

Design of an air traffic computer simulation system to support investigation of civil tiltrotor aircraft operations

The TATSS Project's goal was to develop a design for computer software that would support the attainment of the following objectives for the air traffic simulation model: (1) Full freedom of movement for each aircraft object in the simulation model. Each aircraft object may follow any designated flight plan or flight path necessary as required by the experiment under consideration. (2) Object position precision up to +/- 3 meters vertically and +/- 15 meters horizontally. (3) Aircraft maneuvering in three space with the object position precision identified above. (4) Air traffic control operations and procedures. (5) Radar, communication, navaid, and landing aid performance. (6) Weather. (7) Ground obstructions and terrain. (8) Detection and recording of separation violations. (9) Measures of performance including deviations from flight plans, air space violations, air traffic control messages per aircraft, and traditional temporal based measures

Automatic Geospatial Data Conflation Using Semantic Web Technologies

Duplicate geospatial data collections and maintenance are an extensive problem across Australia government organisations. This research examines how Semantic Web technologies can be used to automate the geospatial data conflation process. The research presents a new approach where generation of OWL ontologies based on output data models and presenting geospatial data as RDF triples serve as the basis for the solution and SWRL rules serve as the core to automate the geospatial data conflation processes