Construction Tools, Equipment and Safety Signal Identification Implemented into Heavy Civil Lab

The purpose of this project is to begin the development and implementation of a teaching module for commonly used construction tools, equipment, and safety signals into the Construction Management department’s Heavy Civil lab. Using knowledge gained from former and future employers, past construction management classes, and online research I found the most commonly used tools, equipment, and hand signals used on construction sites and created visual representations and descriptions of them. Many graduates of the Construction Management program will find themselves on construction sites and there is no question that workers in the construction field are subject to life threatening hazards on a day to day basis. Knowing how to confidently and effectively identify what certain types of equipment are capable of and being able use non-verbal forms of communication will help mitigate some of the risk associated with being on a construction site. Upon completing the Heavy Civil lab students will have the necessary skills to effectively identify construction tools and equipment and communicate basic non-verbal safety signals that will keep everyone on the job safer

Assessing the Effectiveness of a Problem-Based Computer Modelling Module From the Student\u27s Perspective

The Computer Modelling module delivered to the third year Level 8 Mechanical Engineering students in the Technological University Dublin is marked completely by continual assessment. It was developed using a problem based approach in that the theory of Computer Modelling methods is first explained but is then illustrated by demonstrating its application to the solution of real life problems. It is delivered in a traditional manner for the first six weeks in that the underlying principles and techniques of the finite difference method are covered in lectures and practical assignments are completed in the weekly computer laboratory classes. A problem based approach is adopted for the remaining six weeks of the semester. The students form their own groups of three and choose a unique project from a list supplied to them. The primary aim is to get the students to use numerical modelling to solve practical Engineering problems drawn from many different areas such as thermal processing in the food industry, heat transfer in engines, fluid modelling using ANSYS CFX and vibration analysis of structures and machines using Matlab. The students are assigned a supervisor who meets them for at least 30 minutes each week to advise them and to monitor their progress. Each individual student is held to account for their contribution to the project effort. At the end of the semester, each group must create an A1 poster on their particular topic. They are given a standard template to follow and are advised on the structure including Literature Review, Methodologies, Results and Conclusions. The students are assessed on a ten minute presentation of their project to the module lecturers and their peers. A shorter open session is also held in which the students must present their posters to other staff members and students and a prize is awarded to the best poster. A survey was carried out on a group of 12 students who completed the module in 2013.It includes fourteen questions under the headings: Group Dynamics, Project Management, Poster Presentation and Personal View of the Project. In addition, a focus group with a small number of students who had completed the module in 2012 was conducted independently by the second author. The response of the survey was mainly positive with some negative comments. The comments of the focus were broadly in line with the more positive comments from the survey. The responses from the survey and focus groups are reported and discussed in the paper. The overall conclusion is that in general, the module is perceived to be enjoyable and challenging to complete and it equips the students with useful skills going forward

Web Enabled Embedded Devices

The trend in manufacturing of computerised control systems has been to miniaturise the components while increasing the functionality of the systems. This has led to the development of small inexpensive hand-held computer devices coupled with the availability of a user friendly application development language, Java and public cost-effect communication networks has given the developer a programmable web-enabled embedded device. This paper investigates the steps involved in programming the Tiny InterNet Interface platform and analyses the limitations imposed by miniaturisation on this device

Inequality and the Crisis: The Distributional Impact of Tax Increases and Welfare and Public Sector Pay Cuts

The economic crisis impacts directly on the distribution of income via unemployment and private sector wages, but the way policy responds in seeking to control soaring fiscal deficits is also central to its distributional consequences. Having sketched out the background in terms of inequality trends during Ireland’s boom and the channels through which the recession affects different parts of the income distribution, this paper investigates the distributional impact of the government’s policy response with respect to direct tax, social welfare and public sector pay using the SWITCH tax-benefit model. This provides empirical evidence relevant to future policy choices as efforts to reduce the fiscal deficit continue.

A lexical database for public textual cyberbullying detection

Public textual cyberbullying has become one of the most prevalent issues associated with online safety of young people, particularly on social networks. To address this issue, we argue that the boundaries of what constitutes public textual cyberbullying needs to be first identified and a corresponding linguistically motivated definition needs to be advanced. Thus, we propose a definition of public textual cyberbullying that contains three necessary and sufficient elements: the personal marker, the dysphemistic element and the cyberbullying link between the previous two elements. Subsequently, we argue that one of the cornerstones in the overall process of mitigating the effects of cyberbullying is the design of a cyberbullying lexical database that specifies what linguistic and cyberbullying specific information is relevant to the detection process. In this vein, we propose a novel cyberbullying lexical database based on the definition of public textual cyberbullying. The overall architecture of our cyberbullying lexical database is determined semantically, and, in order to facilitate cyberbullying detection, the lexical entry encapsulates two new semantic dimensions that are derived from our definition: cyberbullying function and cyberbullying referential domain. In addition, the lexical entry encapsulates other semantic and syntactic information, such as sense and syntactic category, information that, not only aids the process of detection, but also allows us to expand the cyberbullying database using WordNet (Miller, 1993)

Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying

[EN] Cyberbullying is a risk associated with the online safety of young people and, in this paper, we address one of its most common implicit forms – negation-based forms. We first describe the role of negation in public textual cyberbullying interaction and identify the cyberbullying constructions that characterise these forms. We then formulate the overall detection mechanism which captures the three necessary and sufficient elements of public textual cyberbullying – the personal marker, the dysphemistic element, and the link between them. Finally, we design rules to detect both overt and covert negation-based forms, and measure their effectiveness using a development dataset, as well as a novel test dataset, across several metrics: accuracy, precision, recall, and the F1-measure. The results indicate that the rules we designed closely resemble the performance of human annotators across all measures.Power, A.; Keane, A.; Nolan, B.; O'neill, B. (2018). Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying. Journal of Computer-Assisted Linguistic Research. 2(1):1-20. doi:10.4995/jclr.2018.891712021Al-garadi, M.A., Varathan, K.D. and Ravana S.D. 2016. "Cybercrime Detection in Online Communications: The Experimental Case of Cyberbullying Detection in the Twitter Network." Computers in Human Behaviour, 63: 433 - 443. https://doi.org/10.1016/j.chb.2016.05.051Allan, K. and Burridge, K. 2006. Forbidden Words: Taboo and Censoring of Language. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511617881Boyd, D. 2007. "Why Youth (Heart) Social Network Sites: The Role of Networked Publics in Teenage Social Life." In MacArthur Foundation Series on Digital Learning, Youth, Identity, and Digital Media, edited by David Buckingham, 1 - 26. Cambridge, MA: MIT Press.Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., and Vakali, A. 2017. "Mean Birds: Detecting Aggression and Bullying on Twitter." Cornell University Library: https://arxiv.org/abs/1702.06877.Chen, Y., Zhou, Y., Zhu, S. and Xu, H. 2012. "Detecting Offensive Language in Social Media to Protect Adolescent Online Safety." Paper presented at the ASE/IEEE International Conference on Social Computing, 71 - 80. Washington, DC, September 3-5. https://doi.org/10.1109/SocialCom-PASSAT.2012.55Dadvar, M., Trieschnigg, D., R. Ordelman, R., and de Jong, F. 2013. "Improving cyberbullying detection with user context." Paper presented at the 35th European conference on Advances in Information Retrieval, 693 - 696. Moscow, March 24-27. https://doi.org/10.1007/978-3-642-36973-5_62de Marneffe, M.C., and Manning, C.D. 2008a. "The Stanford typed dependencies representation." Paper presented at the COLING 2008 Workshop on Cross-framework and Cross-domain Parser Evaluation. Manchester, UK August 23 - 23. https://doi.org/10.3115/1608858.1608859de Marneffe, M.C., and Manning, C. 2008b. "Stanford typed dependencies manual." https://nlp.stanford.edu/software/dependencies_manual.pdf.Dinakar, K., Jones, B., Havasi, C., Lieberman, H., and Picard, R. 2012. "Common sense reasoning for detection, prevention, and mitigation of cyberbullying." ACM Transactions on Interactive Intelligent Systems, 2: 18:1-18:30. https://doi.org/10.1145/2362394.2362400Dooley, J.J., Pyzalski, J., and Cross, D. 2009. "Cyberbullying versus face-to-face bullying - A theoretical and conceptual review." Journal of Psychology, 217: 182-188. https://doi.org/10.1027/0044-3409.217.4.182Goncalves, M. 2011. "Text Classification". In Modern Information Retrieval, the concepts and technology behind search, edited by Ricardo Baeza-Yates and Berthier Ribeiro-Neto, 281 - 336. Pearson Education Limited.Grigg, D.W. 2010. "Cyber-Aggression: Definition and Concept of Cyberbullying." Australian Journal of Guidance and Counselling, 12: 143-156. https://doi.org/10.1375/ajgc.20.2.143Hinduja, S., and Patchin, J.W. 2009. Bullying beyond the schoolyard: preventing and responding to cyber-bullying. Thousand Oaks, CA: Corw2017.Horn, L. R. 1989. A Natural History of Negation. Chicago: University of Chicago Press.Hosseinmardi, H., Han, R., Lv, Q., Mishra, S., and Ghasemianlangroodi, A. 2014a. "Towards Understanding Cyberbullying Behavior in a Semi-Anonymous Social Network." Paper presented at the International Conference on Advances in Social Networks Analysis and Mining. Beijing, August 17-20. https://doi.org/10.1109/ASONAM.2014.6921591Hosseinmardi, H., Rafiq, R. I., Li, S., Yang, Z., Han, R., Lv, Q., and Mishra, S. 2014b. "A Comparison of Common Users across Instagram and Ask.fm to Better Understand Cyberbullying." Paper presented at the 7th International Conference on Social Computing and Networking. Sydney, December 3-5.Huang, Q., Singh, V.K., and Atrey, P.K. 2014. "Cyber Bullying Detection using Social and Textual Analysis." Paper presented at the 3rd International Workshop on Socially-Aware Multimedia, 3 - 6. Orlando, Florida, November 7. https://doi.org/10.1145/2661126.2661133InternetSlang. 2017. "Internet Slang - Internet Dictionary." Accessed October 19. http://www.Internetslang.com/.Kavanagh, P. 2014. "Investigation of Cyberbullying Language & Methods." MSc diss., ITB, Ireland.Kontostathis, A., Reynolds, K., Garron, A. and Edwards, L. 2013. Detecting Cyberbullying: Query Terms and Techniques. Paper presented at the 5th Annual ACM Web Science Conference. Paris, May 2-4. https://doi.org/10.1145/2464464.2464499Langos, C. 2012. "Cyberbullying: The Challenge to Define." Cyberpsychology, Behavior, and Social Networks, 15(6): 285-289. https://doi.org/10.1089/cyber.2011.0588Lawler, J. 2005. "Negation and NPIs." http://www.umich.edu/~jlawler/NPIs.pdfLivingstone, S.,Haddon, L., Görzig, A., and Ólafsson, K. 2011. 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In Modern Information Retrieval, the concepts and technology behind search, edited by Ricardo Baeza-Yates and Berthier Ribeiro-Neto, 203 - 254. Pearson Education Limited.Nitta, T., Masui, F., Ptaszynski, M., Kimura, Y., Rzepka, R., and Araki, K. 2013. "Detecting Cyberbullying Entries on Informal School Websites Based on Category Relevance Maximization." Paper presented at the 6th International Joint Conference on Natural LanguageProcessing. Nagoya, October 14-19.Norvig, P. 2007. "How to Write a Spelling Corrector." Accessed October 19. http://norvig.com/spell-correct.html.Oracle. 2017. Java™ Platform, Standard Edition 9 API Specification. Accessed October 19. https://docs.oracle.com/javase/9/docs/api/index.html?overview-summary.html.Power, A., Keane, A., Nolan, B., and O'Neill, B. 2017. "A Lexical Database for Public Textual Cyberbullying Detection". Special issue of Revista de lenguas para fines específicos, entitled New Insights into Meaning Construction and Knowledge Representation.Ptaszynski, M., Dybala, P., Matsuba, T., Rzepka, R. and Araki, K. 2010. "Machine Learning and Affect Analysis Against Cyber-Bullying." Paper presented at the 36th AISB Annual Convention. March 29- April 1.Ptaszynski, M., Masui, F., Nitta, T., Hatekeyama, S., Kimura, Y., Rzepka, R., and Araki, K. 2016. "Sustainable Cyberbullying Detection with Category-Maximised Relevance of Harmful Phrases and Double-Filtered Automatic Optimisation." International Journal of Child-Computer Interaction, 8: 15 - 30. https://doi.org/10.1016/j.ijcci.2016.07.002Reynolds, K., Kontostathis, A. and Edwards, L. 2011. "Using Machine Learning to Detect Cyberbullying." Paper presented at the 10th International Conference on Machine Learning and Applications Workshops. 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Paper presented at the 1st conference on CAW. Madrid, April 20-24

Executive Mindsets Influencing the Alignment of IT and Strategy

When examining previous research on the IT-business strategy relationship, it becomes evident that a key difficulty for organizations is the alignment of IT and strategy. We find that this alignment can be better understood when examining the heads of the IT and business strategy organizational components, the CIO and the CEO. We propose that a technologist CEO and/or a business savvy CIO will improve the communication and understanding between these components, therefore producing a higher level of strategic alignment. We also propose that the three dimensions of IT capability (which have already been linked business performance), a strong and responsive IT staff, a cost-effective & well-managed IT infrastructure, and an effective IT-business relationship, are direct outcomes of strategically aligned planning. We test our model using the Fortune 1000 insurance firms as our sample. Results indicate that firms with a business savvy CIO are more likely to have a higher IT capability than those without a business savvy CIO