COVID-19 Tracking Applications: A Human-Centric Analysis

The year 2020 will always be remembered with the imprints left by COVID-19 on our lives. While the pandemic has had many undesirable effects for the whole world, one of its biggest side effects has been the fast digital transformation that has taken place, which was already in progress with the Industry 4.0 era. The readily available technology and wireless communications infrastructures paved the way for a myriad of digital technologies for the containment of the disease using mobile contact tracing applications developed by health authority organizations in many countries. The mounting privacy concerns especially with Bluetooth-enabled proximity tracing and centralized tracking technologies used by these applications have given rise to the development of new privacy-preserving contact tracing protocols. Although these new protocols have alleviated the privacy concerns of citizens to a certain extent, widespread adoption is still far from being the reality. In this paper, we analyze existing contact tracing technologies from a human-centric standpoint by focusing on their privacy implications. We present our comprehensive dataset consisting of the contact tracing application usage information in 94 countries and provide results of a multinational survey we have conducted on the sentiments of people regarding contact tracing applications. The survey results demonstrate that privacy concerns are still the leading deterrent for people when deciding whether to use these applications. Nevertheless, it is a globally accepted argument that the most effective and fastest method for contact tracking will be digital technologies free from human errors and manual procedures. Accordingly, it is concluded that a policy of developing decentralized tracking solutions based entirely on user privacy should be followed, in which independent trusted third parties assume the role of authority in the system architecture, if absolutely necessary, in order to effectively combat the pandemic worldwide. An important feature of the systems to be developed to pave the way for widespread use is to provide the users the right to be forgotten

Evolutionary Multiobjective Feature Selection for Sentiment Analysis

AuthorSentiment analysis is one of the prominent research areas in data mining and knowledge discovery, which has proven to be an effective technique for monitoring public opinion. The big data era with a high volume of data generated by a variety of sources has provided enhanced opportunities for utilizing sentiment analysis in various domains. In order to take best advantage of the high volume of data for accurate sentiment analysis, it is essential to clean the data before the analysis, as irrelevant or redundant data will hinder extracting valuable information. In this paper, we propose a hybrid feature selection algorithm to improve the performance of sentiment analysis tasks. Our proposed sentiment analysis approach builds a binary classification model based on two feature selection techniques: an entropy-based metric and an evolutionary algorithm. We have performed comprehensive experiments in two different domains using a benchmark dataset, Stanford Sentiment Treebank, and a real-world dataset we have created based on World Health Organization (WHO) public speeches regarding COVID-19. The proposed feature selection model is shown to achieve significant performance improvements in both datasets, increasing classification accuracy for all utilized machine learning and text representation technique combinations. Moreover, it achieves over 70% reduction in feature size, which provides efficiency in computation time and space

EPICS: A Framework for Enforcing Security Policies in Composite Web Services

With advances in cloud computing and the emergence of service marketplaces, the popularity of composite services marks a paradigm shift from single-domain monolithic systems to cross-domain distributed services, which raises important privacy and security concerns. Access control becomes a challenge in such systems because authentication, authorization and data disclosure may take place across endpoints that are not known to clients. The clients lack options for specifying policies to control the sharing of their data and have to rely on service providers which provide limited selection of security and privacy preferences. This lack of awareness and loss of control over data sharing increases threats to a client\u27s data and diminishes trust in these systems

Autonomous Agents-based Mobile-Cloud Computing

The proliferation of cloud computing resources in recent years offers a way for mobile devices with limited resources to achieve computationally intensive tasks in real-time. The mobile-cloud computing paradigm, which involves collaboration of mobile and cloud resources in such tasks, is expected to become increasingly popular in mobile application development. While mobile-cloud computing is promising to overcome the computational limitations of mobile devices, the lack of frameworks compatible with standard technologies makes it harder to adopt dynamic mobile-cloud computing at large. In this dissertation, we present a dynamic code offloading framework for mobile-cloud computing, based on autonomous agents. Our approach does not impose any requirements on the cloud platform other than providing isolated execution containers, and it alleviates the management burden of offloaded code by the mobile platform using autonomous agent-based application partitions. We also investigate the effects of different runtime environment conditions on the performance of mobile-cloud computing, and present a simple and low-overhead dynamic makespan estimation model for computation offloaded to the cloud that can be integrated into mobile agents to enhance them with self-performance evaluation capability. Offloading mobile computation to the cloud entails security risks associated with handing sensitive data and code over to an untrusted platform. Security frameworks for mobile-cloud computing are not very numerous and most of them focus only on privacy, and ignore the very important aspect of integrity. Perfect security is hard to achieve in real-time mobile-cloud computing due to the extra computational overhead introduced by complex security mechanisms. In this dissertation, we propose a dynamic tamper-resistance approach for protecting mobile computation offloaded to the cloud, by augmenting mobile agents with self-protection capability. The tamper-resistance framework achieves very low execution time overhead and is capable of detecting both load-time and runtime modifications to agent code. Lastly, we propose novel applications of mobile-cloud computing for helping context-aware navigation by visually-impaired people. Specifically, we present the results of a feasibility study for using real-time mobile-cloud computing for the task of guiding blind users at pedestrian crossings with no accessible pedestrian signal

A Mobile-Cloud Collaborative Traffic Lights Detector for Blind Navigation

Abstract—Context-awareness is a critical aspect of safe navigation, especially for the blind and visually-impaired in unfamiliar environments. Existing mobile devices for contextaware navigation fall short in many cases due to their dependence on specific infrastructure requirements as well as having limited access to resources that could provide a wealth of contextual clues. In this work, we propose a mobile-cloud collaborative approach for context-aware navigation, where we aim to exploit the computational power of resources made available by Cloud Computing providers as well as the wealth of location-specific resources available on the Internet to provide maximal context-awareness. The system architecture we propose also has the advantages of being extensible and having minimal infrastructural reliance, thus allowing for wide usability. A traffic lights detector was developed as an initial application component of the proposed system and experiments performed to test appropriateness for the realtime nature of the problem. Keywords-mobile; cloud; navigation; context-awareness I