Automated Screening for Three Inborn Metabolic Disorders: A Pilot Study

Background: Inborn metabolic disorders (IMDs) form a large group of rare, but often serious, metabolic disorders. Aims: Our objective was to construct a decision tree, based on classification algorithm for the data on three metabolic disorders, enabling us to take decisions on the screening and clinical diagnosis of a patient. Settings and Design: A non-incremental concept learning classification algorithm was applied to a set of patient data and the procedure followed to obtain a decision on a patient’s disorder. Materials and Methods: Initially a training set containing 13 cases was investigated for three inborn errors of metabolism. Results: A total of thirty test cases were investigated for the three inborn errors of metabolism. The program identified 10 cases with galactosemia, another 10 cases with fructosemia and the remaining 10 with propionic acidemia. The program successfully identified all the 30 cases. Conclusions: This kind of decision support systems can help the healthcare delivery personnel immensely for early screening of IMDs

Troping the Enemy: Metaphor, Culture, and the Big Data Black Boxes of National Security

This article considers how cultural understanding is being brought into the work of the Intelligence Advanced Research Projects Activity (IARPA), through an analysis of its Metaphor program. It examines the type of social science underwriting this program, unpacks implications of the agency’s conception of metaphor for understanding so-called cultures of interest, and compares IARPA’s to competing accounts of how metaphor works to create cultural meaning. The article highlights some risks posed by key deficits in the Intelligence Community\u27s (IC) approach to culture, which relies on the cognitive linguistic theories of George Lakoff and colleagues. It also explores the problem of the opacity of these risks for analysts, even as such predictive cultural analytics are becoming a part of intelligence forecasting. This article examines the problem of information secrecy in two ways, by unpacking the opacity of “black box,” algorithm-based social science of culture for end users with little appreciation of their potential biases, and by evaluating the IC\u27s nontransparent approach to foreign cultures, as it underwrites national security assessments

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." 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"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

The Racing Mind and the Path of Love: automatic extraction of image schematic triggers in knowledge graphs generated from natural language

Embodied Cognition and Cognitive Metaphors Theory take their origin from our use of language: sensorimotor triggers are disseminated in our daily communication, expression and commonsense knowledge. We propose, in this work, a first attempt of image-schematic triggers automatic extraction, starting from knowledge graphs automatically generated from natural language. The methodology proposed here is conceived as a modular addition integrated in the FRED tool, able to generate knowledge graphs from natural language, while it has its foundation in querying ImageSchemaNet, the Image Schematic layer developed on top of FrameNet and integrated in the Framester resource. This methodology allows the extraction of sensorimotor triggers from WordNet, VerbNet, MetaNet, BabelNet and many more

MetaNet: automated dynamic selection of scheduling policies in cloud environments

Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments. In order to sustain the rapid growth of computational demands, one of the most important QoS metrics for cloud schedulers is the execution cost. In this regard, several data-driven deep neural networks (DNNs) based schedulers have been proposed in recent years to allow scalable and efficient resource management in dynamic workload settings. However, optimal scheduling frequently relies on sophisticated DNNs with high computational needs implying higher execution costs. Further, even in non-stationary environments, sophisticated schedulers might not always be required and we could briefly rely on low-cost schedulers in the interest of cost-efficiency. Therefore, this work aims to solve the non-trivial meta problem of online dynamic selection of a scheduling policy using a surrogate model called MetaNet. Unlike traditional solutions with a fixed scheduling policy, MetaNet on-the-fly chooses a scheduler from a large set of DNN based methods to optimize task scheduling and execution costs in tandem. Compared to state-of-the-art DNN schedulers, this allows for improvement in execution costs, energy consumption, response time and service level agreement violations by up to 11, 43, 8 and 13 percent, respectively

Macroscopic Traffic Flow Model Calibration Using Different Optimization Algorithms

AbstractThis study tests and compares different optimization algorithms employed for the calibration of a macroscopic traffic flow model. In particular, the deterministic Nelder-Mead algorithm, a stochastic genetic algorithm and the stochastic cross-entropy method are utilized to estimate the parameter values of the METANET model for a particular freeway site, using real traffic data. The resulting models are validated using various traffic data sets and the optimization algorithms are evaluated and compared with respect to the accuracy of the produced models as well as the convergence speed and the required computation time

Metaphor and Senses

The book deals with the synesthetic metaphors in Synamet – a semantically and grammatically annotated corpus. The texts included in the corpus are excerpted from blogs devoted to, among others, perfume, wine, beer, music, art, massage and wellness. The thesis presents a Conceptual Metaphor Theory (CMT) and frame-based analysis of synesthetic metaphors in Polish. Using data from the corpus, the book provides ample empirical support for embodiment in metaphor and internal logic of mappings between frames. The study proposes new models of verbal synesthesia in the corpus and calls into question a universality of hierarchy of senses. This book should be of interest to researchers working within cognitive linguistics, in particular metaphor theory, frame semantics, corpus linguistics, and sensory science