Joe Tekli, R. Chbeir, A. Traina, C. Traina, K. Yétongnon, Carlos Arturo Raymundo Ibañez, Christian Kallas
{"title":"Upgraded SemIndex Prototype Supporting Intelligent Database Keyword Queries through Disambiguation, Query as You Type, and Parallel Search Algorithms","authors":"Joe Tekli, R. Chbeir, A. Traina, C. Traina, K. Yétongnon, Carlos Arturo Raymundo Ibañez, Christian Kallas","doi":"10.1109/ICCC.2018.00012","DOIUrl":"https://doi.org/10.1109/ICCC.2018.00012","url":null,"abstract":"This paper describes an upgraded version of the SemIndex prototype system for semantic-aware search in textual SQL databases. Semantic-aware querying has emerged as a required extension of the standard containment keyword-based query to meet user needs in textual databases and IR applications. Here, we build on top of SemIndex, a semantic-aware inverted index previously developed by our team, to allow semantic-aware search, result selection, and result ranking functionality. Various weighting functions and intelligent search algorithms have been developed for that purpose and will be presented here. A graphical interface was also added to help end-users write and execute queries. Preliminary experiments highlight SemIndex querying effectiveness and efficiency, considering different querying algorithms, different semantic coverages, and a varying number of query keywords.","PeriodicalId":306012,"journal":{"name":"2018 IEEE International Conference on Cognitive Computing (ICCC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123540723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher Bellman, Miguel Vargas Martin, Shane MacDonald
{"title":"(WKSP) On the Potential of Data Extraction by Detecting Unaware Facial Recognition with Brain-Computer Interfaces","authors":"Christopher Bellman, Miguel Vargas Martin, Shane MacDonald","doi":"10.1109/ICCC.2018.00022","DOIUrl":"https://doi.org/10.1109/ICCC.2018.00022","url":null,"abstract":"Consumer-grade brain-computer interfaces are becoming more readily available to consumers. Directly reading biological information opens the door for an individual to unwillingly expose personal information. Attackers may be able to glean private information based on the level of recognition a victim has to a specific face, and use that to their advantage. In this work, we use a variety of classification algorithms to classify two types of facial recognition: unaware and aware. To do this, source data is manipulated into two datasets for classification: A set of combined and averaged EEG data, and a set of combined EEG data. We find that in all cases, the combined dataset outperforms the combined and averaged dataset. Further, based on the promising results obtained, there's a risk that a malicious third party could utilize similar techniques to extract private information from individuals without their consent using brain-computer interfaces.","PeriodicalId":306012,"journal":{"name":"2018 IEEE International Conference on Cognitive Computing (ICCC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121129094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"PIN Prototype for Intelligent Nutrition Assessment and Meal Planning","authors":"G. Salloum, E. Semaan, Joe Tekli","doi":"10.1109/ICCC.2018.00024","DOIUrl":"https://doi.org/10.1109/ICCC.2018.00024","url":null,"abstract":"This paper briefly describes and evaluates Personal Intelligent Nutritionist (or PIN), a prototype system for intelligent nutrition assessment and meal planning. It aims to automate the two main services offered by a nutrition expert, performing human-like: i) patient health state assessment, and ii) meal plan generation. Preliminary results produced based on 16 real-case human test subjects highlight the effectiveness and efficiency of the solution.","PeriodicalId":306012,"journal":{"name":"2018 IEEE International Conference on Cognitive Computing (ICCC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123091288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Divam Gupta, Indira Sen, Niharika Sachdeva, P. Kumaraguru, Arun Balaji Buduru
{"title":"Empowering First Responders through Automated Multimodal Content Moderation","authors":"Divam Gupta, Indira Sen, Niharika Sachdeva, P. Kumaraguru, Arun Balaji Buduru","doi":"10.1109/ICCC.2018.00008","DOIUrl":"https://doi.org/10.1109/ICCC.2018.00008","url":null,"abstract":"Social media enables users to spread information and opinions, including in times of crisis events such as riots, protests or uprisings. Sensitive event-related content can lead to repercussions in the real world. Therefore it is crucial for first responders, such as law enforcement agencies, to have ready access, and the ability to monitor the propagation of such content. Obstacles to easy access include a lack of automatic moderation tools targeted for first responders. Efforts are further complicated by the multimodal nature of content which may have either textual and pictorial aspects. In this work, as a means of providing intelligence to first responders, we investigate automatic moderation of sensitive event-related content across the two modalities by exploiting recent advances in Deep Neural Networks (DNN). We use a combination of image classification with Convolutional Neural Networks (CNN) and text classification with Recurrent Neural Networks (RNN). Our multilevel content classifier is obtained by fusing the image classifier and the text classifier. We utilize feature engineering for preprocessing but bypass it during classification due to our use of DNNs while achieving coverage by leveraging community guidelines. Our approach maintains a low false positive rate and high precision by learning from a weakly labeled dataset and then, by learning from an expert annotated dataset. We evaluate our system both quantitatively and qualitatively to gain a deeper understanding of its functioning. Finally, we benchmark our technique with current approaches to combating sensitive content and find that our system outperforms by 16% in accuracy.","PeriodicalId":306012,"journal":{"name":"2018 IEEE International Conference on Cognitive Computing (ICCC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128084846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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