The introduction of the Internet of Things (IoT) in education, which allows Internet based communications to happen between physical objects, sensors and controllers, has changed educational institutions massively. By embedding sensors in objects and integrating cloud computing, augmented reality, wearable technologies and big data in this platform, different parameters of the educational environment can be measured and analysed to provide useful information. It also has created a new interaction between people and the environment in educational organisation. In this research based on the recent IoT projects in education, we will categorize the application of IoT in education into four groups: energy management and real time ecosystem monitoring, monitoring student's healthcare, classroom access control and improving teaching and learning. We will investigate and analyse how this platform has changed the Education Business Model and added new value propositions in such organizations based on the Canvas Business Model.
{"title":"The Effect of the Internet of Things (IoT) on Education Business Model","authors":"Maryam Bagheri, Siavosh H. Movahed","doi":"10.1109/SITIS.2016.74","DOIUrl":"https://doi.org/10.1109/SITIS.2016.74","url":null,"abstract":"The introduction of the Internet of Things (IoT) in education, which allows Internet based communications to happen between physical objects, sensors and controllers, has changed educational institutions massively. By embedding sensors in objects and integrating cloud computing, augmented reality, wearable technologies and big data in this platform, different parameters of the educational environment can be measured and analysed to provide useful information. It also has created a new interaction between people and the environment in educational organisation. In this research based on the recent IoT projects in education, we will categorize the application of IoT in education into four groups: energy management and real time ecosystem monitoring, monitoring student's healthcare, classroom access control and improving teaching and learning. We will investigate and analyse how this platform has changed the Education Business Model and added new value propositions in such organizations based on the Canvas Business Model.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"29 12 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":"125785849","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}
Blind users are facing enormous difficulties in accessing Online Social Networks (OSN). OSN are complex and they have a cluttered structure. The mobile website is much more suitable for screen readers, but the amount of information and functionalities are not the same as on the regular website. This paper develops a browser extension to restructure Facebook's regular homepage to an accessible version. For that, the current state of the regular website has been analyzed to identify shortcomings, then the target state has been developed, considering HTML 5 and HCI guidelines, and it has been tested by a blind person.
{"title":"An Analysis to Overcome Shortcomings to Improve the Accessibility for the Blind: A Case Study on Facebook's Homepage","authors":"Petra Gröber, Julia Koster","doi":"10.1109/SITIS.2016.75","DOIUrl":"https://doi.org/10.1109/SITIS.2016.75","url":null,"abstract":"Blind users are facing enormous difficulties in accessing Online Social Networks (OSN). OSN are complex and they have a cluttered structure. The mobile website is much more suitable for screen readers, but the amount of information and functionalities are not the same as on the regular website. This paper develops a browser extension to restructure Facebook's regular homepage to an accessible version. For that, the current state of the regular website has been analyzed to identify shortcomings, then the target state has been developed, considering HTML 5 and HCI guidelines, and it has been tested by a blind person.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"23 3 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":"123476250","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}
Ilaria Torre, G. Adorni, Frosina Koceva, Odnan Ref Sanchez
Sharing data among applications is a growing phenomenon. With the IoT, this phenomenon becomes more significant. As already studied in social networks, data sharing has the drawback of privacy risks. Authorization protocols and cryptographic systems may not be enough to ensure that user data and metadata are not used for non-legitimate purposes. There are different scenarios and several personal data management proposals aimed to improve privacy protection. However, a risk that is always present is related to the possibility of processing and aggregating public and authorized data to infer sensitive information and data that the user may not want to share. These approaches, often called inference attacks, concern the disclosure of personal user data and have been widely studied in social networks. In this paper we describe the problem and some techniques to face it, showing its relevance in the IoT. Then we present the concept of an Adaptive Inference Discovery Service AID-S, conceived as a service that may support users to prevent this kind of information leakage and that can be integrated into personal data managers.
{"title":"Preventing Disclosure of Personal Data in IoT Networks","authors":"Ilaria Torre, G. Adorni, Frosina Koceva, Odnan Ref Sanchez","doi":"10.1109/SITIS.2016.68","DOIUrl":"https://doi.org/10.1109/SITIS.2016.68","url":null,"abstract":"Sharing data among applications is a growing phenomenon. With the IoT, this phenomenon becomes more significant. As already studied in social networks, data sharing has the drawback of privacy risks. Authorization protocols and cryptographic systems may not be enough to ensure that user data and metadata are not used for non-legitimate purposes. There are different scenarios and several personal data management proposals aimed to improve privacy protection. However, a risk that is always present is related to the possibility of processing and aggregating public and authorized data to infer sensitive information and data that the user may not want to share. These approaches, often called inference attacks, concern the disclosure of personal user data and have been widely studied in social networks. In this paper we describe the problem and some techniques to face it, showing its relevance in the IoT. Then we present the concept of an Adaptive Inference Discovery Service AID-S, conceived as a service that may support users to prevent this kind of information leakage and that can be integrated into personal data managers.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"18 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":"121815850","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}
The differential count and analysis of blood cells in microscope images can provide useful information concerning the health of patients. There are three major blood cell types, namely, erythrocytes (RBCs), leukocytes (WBCs), and platelets. Automated blood cell analysers can provide RBCs, WBCs and platelets count but the presence of abnormal cells could affect the cells counting, that should be checked manually. This is why today the conventional practice for such procedure is executed manually by pathologists under light microscope. However, the manual visual inspection is tedious, time consuming, repetitive and it is strongly influenced by the operator's capabilities and tiredness. Therefore, a good clinical decision support system for cells counting and classification has always become a necessity. Few examples of automated systems that can analyse and classify blood cells have been reported in the literature. This research proposes a computer-aided systems that simulates a human visual inspection to automate the process of detection and identification of WBCs and RBCs from blood smear images. The proposed method has been tested on public datasets of blood cell images and demonstrates a reliable and effective system for differential counting, obtaining an average accuracy value of 99.2% for WBCs and 98% for RBCs, outperforming the state-of-the-art.
{"title":"A Computer-Aided System for Differential Count from Peripheral Blood Cell Images","authors":"A. Loddo, Lorenzo Putzu, C. D. Ruberto, G. Fenu","doi":"10.1109/SITIS.2016.26","DOIUrl":"https://doi.org/10.1109/SITIS.2016.26","url":null,"abstract":"The differential count and analysis of blood cells in microscope images can provide useful information concerning the health of patients. There are three major blood cell types, namely, erythrocytes (RBCs), leukocytes (WBCs), and platelets. Automated blood cell analysers can provide RBCs, WBCs and platelets count but the presence of abnormal cells could affect the cells counting, that should be checked manually. This is why today the conventional practice for such procedure is executed manually by pathologists under light microscope. However, the manual visual inspection is tedious, time consuming, repetitive and it is strongly influenced by the operator's capabilities and tiredness. Therefore, a good clinical decision support system for cells counting and classification has always become a necessity. Few examples of automated systems that can analyse and classify blood cells have been reported in the literature. This research proposes a computer-aided systems that simulates a human visual inspection to automate the process of detection and identification of WBCs and RBCs from blood smear images. The proposed method has been tested on public datasets of blood cell images and demonstrates a reliable and effective system for differential counting, obtaining an average accuracy value of 99.2% for WBCs and 98% for RBCs, outperforming the state-of-the-art.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"74 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":"123805026","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}
The eyes are the most preeminent features of the human face and the ability to accurate the eye landmarks is crucial to a variety of application domains. In this paper, we present a probabilistic method to detect the eye shape in periocular images based on particle filters. The proposed method does not need any prior information about the position of the iris and there is no need for initialization. The eyes are modeled by a simple feature vector that generates two parabolas for the upper and lower eyelid. In order to ensure the robustness of the solution, several measurement cues are fused together when computing the score of a hypothetical eye shape. The proposed method was extensively evaluated on a publicly available database.
{"title":"Eye Shape and Corners Detection in Periocular Images Using Particle Filters","authors":"D. Borza, R. Danescu","doi":"10.1109/SITIS.2016.12","DOIUrl":"https://doi.org/10.1109/SITIS.2016.12","url":null,"abstract":"The eyes are the most preeminent features of the human face and the ability to accurate the eye landmarks is crucial to a variety of application domains. In this paper, we present a probabilistic method to detect the eye shape in periocular images based on particle filters. The proposed method does not need any prior information about the position of the iris and there is no need for initialization. The eyes are modeled by a simple feature vector that generates two parabolas for the upper and lower eyelid. In order to ensure the robustness of the solution, several measurement cues are fused together when computing the score of a hypothetical eye shape. The proposed method was extensively evaluated on a publicly available database.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"39 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":"116624715","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}
In this paper we consider sources of enhancement for the Smoothed Particle Hydrodynamics method in approximating a function and its derivatives. It is well known that the standard formulation is usually poor when scattered data distribution is considered or when the approximation near the boundary occurs. In this paper studies on the accuracy are provided and assessed with gridded and scattered data distribution in the problem domain. The improvements of the method are addressed and supporting numerical experiments are included.
{"title":"Some Numerical Remarks on a Meshless Approximation Method","authors":"E. Francomano, G. Micale, M. Paliaga, G. Ala","doi":"10.1109/SITIS.2016.116","DOIUrl":"https://doi.org/10.1109/SITIS.2016.116","url":null,"abstract":"In this paper we consider sources of enhancement for the Smoothed Particle Hydrodynamics method in approximating a function and its derivatives. It is well known that the standard formulation is usually poor when scattered data distribution is considered or when the approximation near the boundary occurs. In this paper studies on the accuracy are provided and assessed with gridded and scattered data distribution in the problem domain. The improvements of the method are addressed and supporting numerical experiments are included.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"686 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":"122215791","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}
Human creativity is assessed with a variety of tools, one of which is the Remote Associates Test. Two linguistic variants of this test exist: the compound and the functional Remote Associates Test. While normative data and solvers for the compound RAT exist, data sets of functional items are very rarely encountered in the literature. Such data sets would allow (i) a deeper understanding and the simulation of the cognitive associative processes used in creativity tasks and (ii) the comparison of performance and process between the two types of queries. In this paper, an approach to knowledge acquisition and computational generation of functional Remote Associates Test items is explored. Possibilities of cognitive evaluation are discussed.
{"title":"Towards Using Cognitive Word Associates to Create Functional Remote Associates Test Problems","authors":"Ana-Maria Olteţeanu","doi":"10.1109/SITIS.2016.101","DOIUrl":"https://doi.org/10.1109/SITIS.2016.101","url":null,"abstract":"Human creativity is assessed with a variety of tools, one of which is the Remote Associates Test. Two linguistic variants of this test exist: the compound and the functional Remote Associates Test. While normative data and solvers for the compound RAT exist, data sets of functional items are very rarely encountered in the literature. Such data sets would allow (i) a deeper understanding and the simulation of the cognitive associative processes used in creativity tasks and (ii) the comparison of performance and process between the two types of queries. In this paper, an approach to knowledge acquisition and computational generation of functional Remote Associates Test items is explored. Possibilities of cognitive evaluation are discussed.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"33 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":"129508712","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}
This paper describes different approaches for detection and identification of diseases in apples using computer vision. Our proposed algorithms analyze surface appearance of apple for defects using image features, viz. color and texture. For segmentation of Region Of Interest (ROI), K-means clustering is performed over the image pixels based on their intensity values. For creation of feature vector, combinations of Gabor Wavelets with different feature descriptors were explored. Comparative study has been carried out between Haralick features, Local Binary Patterns, and kernel PCA, to observe their performance over Gabor features. Classification is achieved via Support Vector Machines and K-Nearest Neighbors. For the task of disease detection, accuracy recorded was greater than 96.9% for Gabor+LBP approach and in range of 89.8% to 96.25% for Gabor+Haralick approach. Gabor+kernel PCA recorded lowest accuracy of 90%. For disease identification, combination of Gabor+LBP outperformed other combinations, recording highest accuracy ranging from 85.93% to 95.31%.
{"title":"Analyzing Surface Defects in Apples Using Gabor Features","authors":"P. Jolly, S. Raman","doi":"10.1109/SITIS.2016.36","DOIUrl":"https://doi.org/10.1109/SITIS.2016.36","url":null,"abstract":"This paper describes different approaches for detection and identification of diseases in apples using computer vision. Our proposed algorithms analyze surface appearance of apple for defects using image features, viz. color and texture. For segmentation of Region Of Interest (ROI), K-means clustering is performed over the image pixels based on their intensity values. For creation of feature vector, combinations of Gabor Wavelets with different feature descriptors were explored. Comparative study has been carried out between Haralick features, Local Binary Patterns, and kernel PCA, to observe their performance over Gabor features. Classification is achieved via Support Vector Machines and K-Nearest Neighbors. For the task of disease detection, accuracy recorded was greater than 96.9% for Gabor+LBP approach and in range of 89.8% to 96.25% for Gabor+Haralick approach. Gabor+kernel PCA recorded lowest accuracy of 90%. For disease identification, combination of Gabor+LBP outperformed other combinations, recording highest accuracy ranging from 85.93% to 95.31%.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"1 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":"130689901","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}
Emad-ul-Haq Qazi, M. Hussain, Hatim Aboalsamh, Wadood Abdul, Saeed Bamatraf, I. Ullah
Epilepsy is a neurological disorder disease that affects more than 55 million people in the world. In this paper, we have proposed an efficient intelligent pattern recognition system for the classification of epileptic and non-epileptic electroencephalogram (EEG) signals. For this purpose, we used state-of-the-art machine learning technique, i.e., SVM (support vector machines) to classify epileptic and non-epileptic signals. Two (02) different classes of signals are used in this study, i.e., non-epileptic with open eyes and epileptic in seizure condition. One hundred (100) subjects from each class were employed for extraction of discriminatory features and classification purpose. After pre-processing of EEG signals, we use discrete wavelet transform (DWT) to decompose signals upto level 5. Then various features, i.e., energy, entropy and standard deviation are extracted from wavelet bands. Next, we use these features in the classification of signals. We achieved the classification accuracy of 100 % at delta band (0 to 3 Hz) and theta band (3 to 6 Hz). The comparisons with the previous studies show the significance of this system, which can be utilized in real-time as well as in offline clinical applications.
{"title":"An Intelligent System to Classify Epileptic and Non-Epileptic EEG Signals","authors":"Emad-ul-Haq Qazi, M. Hussain, Hatim Aboalsamh, Wadood Abdul, Saeed Bamatraf, I. Ullah","doi":"10.1109/SITIS.2016.44","DOIUrl":"https://doi.org/10.1109/SITIS.2016.44","url":null,"abstract":"Epilepsy is a neurological disorder disease that affects more than 55 million people in the world. In this paper, we have proposed an efficient intelligent pattern recognition system for the classification of epileptic and non-epileptic electroencephalogram (EEG) signals. For this purpose, we used state-of-the-art machine learning technique, i.e., SVM (support vector machines) to classify epileptic and non-epileptic signals. Two (02) different classes of signals are used in this study, i.e., non-epileptic with open eyes and epileptic in seizure condition. One hundred (100) subjects from each class were employed for extraction of discriminatory features and classification purpose. After pre-processing of EEG signals, we use discrete wavelet transform (DWT) to decompose signals upto level 5. Then various features, i.e., energy, entropy and standard deviation are extracted from wavelet bands. Next, we use these features in the classification of signals. We achieved the classification accuracy of 100 % at delta band (0 to 3 Hz) and theta band (3 to 6 Hz). The comparisons with the previous studies show the significance of this system, which can be utilized in real-time as well as in offline clinical applications.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"25 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":"130865626","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}
In this paper, we shall critically appraise sparse representation based denoising applications. An essential task for this framework is dictionary learning. Our novel proposition involves learning such a dictionary not only by analyzing the distribution of training data in the metric space but also exploiting local nature of the visual scene. Subsequently, the learning scheme is further developed for a message passing interface programming architecture. The resulting algorithm is applied to gray scale image denoising which one of the fundamental problems in image processing. In this regard, we show that dictionary learning from noisy images improves denoising performance. Experimental results indicate that proposed approach outperforms the exact KSVD denoising approach and for some cases even surpasses BM3D based denoising.
{"title":"Sparse Multi-Model Based Denoising","authors":"Rajesh Bhatt, V. Subramanian","doi":"10.1109/SITIS.2016.30","DOIUrl":"https://doi.org/10.1109/SITIS.2016.30","url":null,"abstract":"In this paper, we shall critically appraise sparse representation based denoising applications. An essential task for this framework is dictionary learning. Our novel proposition involves learning such a dictionary not only by analyzing the distribution of training data in the metric space but also exploiting local nature of the visual scene. Subsequently, the learning scheme is further developed for a message passing interface programming architecture. The resulting algorithm is applied to gray scale image denoising which one of the fundamental problems in image processing. In this regard, we show that dictionary learning from noisy images improves denoising performance. Experimental results indicate that proposed approach outperforms the exact KSVD denoising approach and for some cases even surpasses BM3D based denoising.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"21 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":"133085083","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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