By Roger Lee (eds.)
This edited e-book provides clinical result of the 13th IEEE/ACIS foreign convention on computing device and knowledge technology (ICIS 2014) which was once hung on June 4-6, 2014 in Taiyuan, China. the purpose of this convention was once to collect researchers and scientists, businessmen and marketers, academics, engineers, computing device clients, and scholars to debate the varied fields of desktop technology and to percentage their reviews and alternate new principles and knowledge in a significant approach. examine effects approximately all features (theory, functions and instruments) of computing device and data technological know-how, and to debate the sensible demanding situations encountered alongside the way in which and the ideas followed to resolve them.
The convention organizers chosen the easiest papers from these papers authorized for presentation on the convention. The papers have been selected in response to evaluation rankings submitted through contributors of this system committee, and underwent extra rigorous rounds of overview. This ebook captures 14 of the conference’s so much promising papers, and we impatiently watch for the $64000 contributions that we all know those authors will convey to the sector of machine and data science.
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Then, we assign it to the closest cluster. Finally, the final decision is obtained by combining the four partial confidence values using the aggregation weights of the closest cluster. Table 1 shows the confusion matrix obtained using the three single classifiers, the Method in , and the proposed method. One can notice that the proposed method outperforms the other approaches in terms of Specificity. More specifically, our approach detected about 50 % more insult comments than single classifiers.
Smokey: Automatic recognition of hostile messages. In: Proceedings of the Ninth Conference on Innovative Applications of Artificial Intelligence, pp. 1058–1065 (1997) 2. , Khan, M, Detecting flames and insults in text. In: Proceedings of the Sixth International Conference on Natural Language Processing (2008) 3. , Offensive language detection using multilevel classification. In: Proceedings of the 23rd Canadian Conference on Artificial Intelligence, pp. 16–27 (2010) 4. , & Rosé, C. P. , Detecting Offensive Tweets via Topical Feature Discovery over a Large Scale Twitter Corpus, Proceedings of The 21st ACM Conference on Information and Knowledge Management, Sheraton, Maui Hawaii, October 29–November 2, (2012) 5.
Nevertheless, with the growth in the use of mobile technologies and the learning scenario database, an increasing number of teachers want to reuse their scenario in different contexts. Instead of adding to the already large number of checklists for learning scenario evaluation, we are attempting, in the following section, to address contextual evaluation based on formal modeling and verification of educational scenario. Next, the evaluation framework is discussed. 1 ReStart-Me Challenges In order to fill the gap between learning scenarios evaluation methods and ubiquitous learning scenarios, we present in this paper, a formal method to evaluate scenarios at an early stage: ReStart-Me (Re-engineering educational Scenario based on timed automata and tracks treatment for Malleable learning environment).