Pub Date : 2019-06-01DOI: 10.1109/IACS.2019.8809145
Inas Abuqaddom, Hadeel Alazzam, A. Hudaib, F. Al-Zaghoul
Website usability is one of the most important quality factors which cannot be measured easily, because of its dependency on various other factors, which some of them are difficult to be measured. Literature shows several website usability models which do not include all usability aspects and shows the difficulty of measuring usability. This paper proposes a website hierarchical usability model with 9 major factors and 24 measurable criteria which are distributed and replicated among factors in hierarchical manner to achieve weight concept. Also this paper introduces a case study Jordan University Website with free tools to measure its usability.
{"title":"A measurable website usability model: Case Study University of Jordan","authors":"Inas Abuqaddom, Hadeel Alazzam, A. Hudaib, F. Al-Zaghoul","doi":"10.1109/IACS.2019.8809145","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809145","url":null,"abstract":"Website usability is one of the most important quality factors which cannot be measured easily, because of its dependency on various other factors, which some of them are difficult to be measured. Literature shows several website usability models which do not include all usability aspects and shows the difficulty of measuring usability. This paper proposes a website hierarchical usability model with 9 major factors and 24 measurable criteria which are distributed and replicated among factors in hierarchical manner to achieve weight concept. Also this paper introduces a case study Jordan University Website with free tools to measure its usability.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121618789","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}
Pub Date : 2018-12-13DOI: 10.1109/IACS.2019.8809127
A. Tarawneh, Ahmad Hassanat, C. Celik, D. Chetverikov, Mohammad Sohel Rahman, C. Verma
Facial image retrieval is a challenging task since faces have many similar features (areas), which makes it difficult for the retrieval systems to distinguish faces of different people. With the advent of deep learning, deep networks are often applied to extract powerful features that are used in many areas of computer vision. This paper investigates the application of different deep learning models’ (layers) for face image retrieval, namely, Alexlayer6, Alexlayer7, VGG16layer6, VGG16layer7, VGG19layer6, and VGG19layer7, with two types of dictionary learning techniques, namely K-means and K-SVD. We also investigate some coefficient learning techniques such as the Homotopy, Lasso, Elastic Net and SSF and their effect on the face retrieval system. The comparative results of the experiments conducted on three standard face image datasets show that the best performers for face image retrieval are Alexlayer7 with K-means and SSF, Alexlayer6 with K-SVD and SSF, and Alexlayer6 with K-means and SSF. The APR and ARR of these methods were further compared to some of the state-of-the-art methods based on local descriptors. The experimental results show that deep learning outperforms most of those methods and therefore can be recommended for use in practice of face image retrieval.
{"title":"Deep Face Image Retrieval: a Comparative Study with Dictionary Learning","authors":"A. Tarawneh, Ahmad Hassanat, C. Celik, D. Chetverikov, Mohammad Sohel Rahman, C. Verma","doi":"10.1109/IACS.2019.8809127","DOIUrl":"https://doi.org/10.1109/IACS.2019.8809127","url":null,"abstract":"Facial image retrieval is a challenging task since faces have many similar features (areas), which makes it difficult for the retrieval systems to distinguish faces of different people. With the advent of deep learning, deep networks are often applied to extract powerful features that are used in many areas of computer vision. This paper investigates the application of different deep learning models’ (layers) for face image retrieval, namely, Alexlayer6, Alexlayer7, VGG16layer6, VGG16layer7, VGG19layer6, and VGG19layer7, with two types of dictionary learning techniques, namely K-means and K-SVD. We also investigate some coefficient learning techniques such as the Homotopy, Lasso, Elastic Net and SSF and their effect on the face retrieval system. The comparative results of the experiments conducted on three standard face image datasets show that the best performers for face image retrieval are Alexlayer7 with K-means and SSF, Alexlayer6 with K-SVD and SSF, and Alexlayer6 with K-means and SSF. The APR and ARR of these methods were further compared to some of the state-of-the-art methods based on local descriptors. The experimental results show that deep learning outperforms most of those methods and therefore can be recommended for use in practice of face image retrieval.","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116841354","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}
Pub Date : 1900-01-01DOI: 10.1109/iacs.2019.8809147
Program Vice-Chairs, M. Bender, Ashvin Goel, Chris Gill, Wu-chang Feng, Chenyang Lu, Jie Liu, T. Abdelzaher, K. Almeroth, J. Bacon
{"title":"ICICS 2019 Technical Program Committee","authors":"Program Vice-Chairs, M. Bender, Ashvin Goel, Chris Gill, Wu-chang Feng, Chenyang Lu, Jie Liu, T. Abdelzaher, K. Almeroth, J. Bacon","doi":"10.1109/iacs.2019.8809147","DOIUrl":"https://doi.org/10.1109/iacs.2019.8809147","url":null,"abstract":"","PeriodicalId":225697,"journal":{"name":"2019 10th International Conference on Information and Communication Systems (ICICS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115111517","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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