Jadran Sessa - Academia.edu (original) (raw)

Papers by Jadran Sessa

Research paper thumbnail of Authorization schemes for internet of things: requirements, weaknesses, future challenges and trends

Complex & Intelligent Systems

Scalable and secure authorization of smart things is of the crucial essence for the successful de... more Scalable and secure authorization of smart things is of the crucial essence for the successful deployment of the Internet of Things (IoT). Unauthorized access to smart things could exacerbate the security and privacy concern, which could, in turn, lead to the reduced adoption of the IoT, and ultimately to the emergence of severe threats. Even though there are a variety of IoT solutions for secure authorization, authorization schemes in highly dynamic distributed environments remain a daunting challenge. Access rights can dynamically change due to the heterogeneous nature of shared IoT devices and, thus, the identity and access control management are challenging. This survey provides a comprehensive comparative analysis of the current state-of-the-art IoT authorization schemes to highlight their strengths and weaknesses. Then, it defines the most important requirements and highlights the authorization threats and weaknesses impacting authorization in the IoT. Finally, the survey pres...

Research paper thumbnail of Intelligent Automation of Crime Prediction using Data Mining

2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)

Research paper thumbnail of Towards heat tolerant metagenome functional prediction, coral microbial community composition, and enrichment analysis

Research paper thumbnail of Deep Learning-based Face Mask Usage Detection on Low Compute Resource Devices

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)

The advent of the COVID-19 pandemic has brought several never-before-seen changes in the daily li... more The advent of the COVID-19 pandemic has brought several never-before-seen changes in the daily lives of people around the globe. As a way to curb the spreading of the disease, wearing face masks has become mandatory in the majority of public places. To solve the necessity of face mask detection in such situations, there have been only a handful of research endeavors up to this date. Computer vision has advanced multi-fold with the advent of AlexNet architecture. With a motivation to go deeper with the neural network architecture, the concept of Depthwise Separable Convolutions and projection layer was developed in MobileNetV1. In this work, a novel lightweight deep learning model based on Single Shot Detector (SSD) MobileNetV2 architecture is proposed for face mask detection using images and video streams of crowds aiming its utilization on low compute re-source environment. An open benchmark face mask dataset, with 4095 images including masked and no mask images, is utilized to train the model for detection. The model is initialized using transfer learning with the freezing of base layers. The proposed methodology can efficiently aid in tracking and enforcing social distancing rules in crowded places with the use of surveillance cameras. On the different benchmarks that we have tested, the model proved to be highly successful and has achieved an accuracy rate of 99.39% and an F1 score of 0.995.

Research paper thumbnail of Data Analysis of Correlation Between Project Popularity and Code Change Frequency

Neural Information Processing, 2016

Github is a source code management platform with social networking features that help increase th... more Github is a source code management platform with social networking features that help increase the popularity of a project. The features of the GitHub like watch, star, fork and pull requests help make a project popular among the developers, in addition to enabling them to work on the code together. In this work, we study the relation between the project popularity and the continual code changes made to a GitHub project. The correlation is found by using the metrics such as the number of watchers, pull requests, and the number of commits. We correlate the time series of code change frequency with the time series of project popularity. As a result, we have found that projects with at least 1500 watchers each month have a strong positive correlation between the project popularity and frequency of code changes. We have also found that the number of pull requests is 73.2 % more important to the popularity of a project than the number of watchers.

Research paper thumbnail of Techniques to deal with missing data

2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), 2016

Data is available to us in humongous amounts in the real world, but none of it is of practical us... more Data is available to us in humongous amounts in the real world, but none of it is of practical use if not converted to useful information. However, the knowledge discovery is hindered because the real data is often incomplete and noisy. Nowadays, the problem of recovering missing data has found most important place in the field of data mining. Filling the missing data is a significant task, as it is paramount to use all available data for the given datasets are generally very small. In this paper, we deal with the real data with many missing values. Furthermore, we deal with the given data in three phases. The first phase considers the concept of feature selection, while the second phase iteratively considers filling in the missing values using probabilistic approach, keeping in mind the fact that features can be either nominal or numerical. Finally, the third phase deals with correcting the missing values that have been filled in. In our work, we have compared two imputation method...

Research paper thumbnail of A Data-informed Public Health Policy-Makers Platform

International Journal of Environmental Research and Public Health

Hearing loss is a disease exhibiting a growing trend due to a number of factors, including but no... more Hearing loss is a disease exhibiting a growing trend due to a number of factors, including but not limited to the mundane exposure to the noise and ever-increasing size of the older population. In the framework of a public health policymaking process, modeling of the hearing loss disease based on data is a key factor in alleviating the issues related to the disease and in issuing effective public health policies. First, the paper describes the steps of the data-driven policymaking process. Afterward, a scenario along with the part of the proposed platform responsible for supporting policymaking are presented. With the aim of demonstrating the capabilities and usability of the platform for the policy-makers, some initial results of preliminary analytics are presented in the framework of a policy-making process. Ultimately, the utility of the approach is validated throughout the results of the survey which was presented to the health system policy-makers involved in the policy develop...

Research paper thumbnail of Authorization schemes for internet of things: requirements, weaknesses, future challenges and trends

Complex & Intelligent Systems

Scalable and secure authorization of smart things is of the crucial essence for the successful de... more Scalable and secure authorization of smart things is of the crucial essence for the successful deployment of the Internet of Things (IoT). Unauthorized access to smart things could exacerbate the security and privacy concern, which could, in turn, lead to the reduced adoption of the IoT, and ultimately to the emergence of severe threats. Even though there are a variety of IoT solutions for secure authorization, authorization schemes in highly dynamic distributed environments remain a daunting challenge. Access rights can dynamically change due to the heterogeneous nature of shared IoT devices and, thus, the identity and access control management are challenging. This survey provides a comprehensive comparative analysis of the current state-of-the-art IoT authorization schemes to highlight their strengths and weaknesses. Then, it defines the most important requirements and highlights the authorization threats and weaknesses impacting authorization in the IoT. Finally, the survey pres...

Research paper thumbnail of Intelligent Automation of Crime Prediction using Data Mining

2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)

Research paper thumbnail of Towards heat tolerant metagenome functional prediction, coral microbial community composition, and enrichment analysis

Research paper thumbnail of Deep Learning-based Face Mask Usage Detection on Low Compute Resource Devices

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)

The advent of the COVID-19 pandemic has brought several never-before-seen changes in the daily li... more The advent of the COVID-19 pandemic has brought several never-before-seen changes in the daily lives of people around the globe. As a way to curb the spreading of the disease, wearing face masks has become mandatory in the majority of public places. To solve the necessity of face mask detection in such situations, there have been only a handful of research endeavors up to this date. Computer vision has advanced multi-fold with the advent of AlexNet architecture. With a motivation to go deeper with the neural network architecture, the concept of Depthwise Separable Convolutions and projection layer was developed in MobileNetV1. In this work, a novel lightweight deep learning model based on Single Shot Detector (SSD) MobileNetV2 architecture is proposed for face mask detection using images and video streams of crowds aiming its utilization on low compute re-source environment. An open benchmark face mask dataset, with 4095 images including masked and no mask images, is utilized to train the model for detection. The model is initialized using transfer learning with the freezing of base layers. The proposed methodology can efficiently aid in tracking and enforcing social distancing rules in crowded places with the use of surveillance cameras. On the different benchmarks that we have tested, the model proved to be highly successful and has achieved an accuracy rate of 99.39% and an F1 score of 0.995.

Research paper thumbnail of Data Analysis of Correlation Between Project Popularity and Code Change Frequency

Neural Information Processing, 2016

Github is a source code management platform with social networking features that help increase th... more Github is a source code management platform with social networking features that help increase the popularity of a project. The features of the GitHub like watch, star, fork and pull requests help make a project popular among the developers, in addition to enabling them to work on the code together. In this work, we study the relation between the project popularity and the continual code changes made to a GitHub project. The correlation is found by using the metrics such as the number of watchers, pull requests, and the number of commits. We correlate the time series of code change frequency with the time series of project popularity. As a result, we have found that projects with at least 1500 watchers each month have a strong positive correlation between the project popularity and frequency of code changes. We have also found that the number of pull requests is 73.2 % more important to the popularity of a project than the number of watchers.

Research paper thumbnail of Techniques to deal with missing data

2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), 2016

Data is available to us in humongous amounts in the real world, but none of it is of practical us... more Data is available to us in humongous amounts in the real world, but none of it is of practical use if not converted to useful information. However, the knowledge discovery is hindered because the real data is often incomplete and noisy. Nowadays, the problem of recovering missing data has found most important place in the field of data mining. Filling the missing data is a significant task, as it is paramount to use all available data for the given datasets are generally very small. In this paper, we deal with the real data with many missing values. Furthermore, we deal with the given data in three phases. The first phase considers the concept of feature selection, while the second phase iteratively considers filling in the missing values using probabilistic approach, keeping in mind the fact that features can be either nominal or numerical. Finally, the third phase deals with correcting the missing values that have been filled in. In our work, we have compared two imputation method...

Research paper thumbnail of A Data-informed Public Health Policy-Makers Platform

International Journal of Environmental Research and Public Health

Hearing loss is a disease exhibiting a growing trend due to a number of factors, including but no... more Hearing loss is a disease exhibiting a growing trend due to a number of factors, including but not limited to the mundane exposure to the noise and ever-increasing size of the older population. In the framework of a public health policymaking process, modeling of the hearing loss disease based on data is a key factor in alleviating the issues related to the disease and in issuing effective public health policies. First, the paper describes the steps of the data-driven policymaking process. Afterward, a scenario along with the part of the proposed platform responsible for supporting policymaking are presented. With the aim of demonstrating the capabilities and usability of the platform for the policy-makers, some initial results of preliminary analytics are presented in the framework of a policy-making process. Ultimately, the utility of the approach is validated throughout the results of the survey which was presented to the health system policy-makers involved in the policy develop...