122 research outputs found

    Automated Privacy Protection for Mobile Device Users and Bystanders in Public Spaces

    Get PDF
    As smartphones have gained popularity over recent years, they have provided usersconvenient access to services and integrated sensors that were previously only available through larger, stationary computing devices. This trend of ubiquitous, mobile devices provides unparalleled convenience and productivity for users who wish to perform everyday actions such as taking photos, participating in social media, reading emails, or checking online banking transactions. However, the increasing use of mobile devices in public spaces by users has negative implications for their own privacy and, in some cases, that of bystanders around them. Specifically, digital photography trends in public have negative implications for bystanders who can be captured inadvertently in users’ photos. Those who are captured often have no knowledge of being photographed and have no control over how photos of them are distributed. To address this growing issue, a novel system is proposed for protecting the privacy of bystanders captured in public photos. A fully automated approach to accurately distinguish the intended subjects from strangers is explored. A feature-based classification scheme utilizing entire photos is presented. Additionally, the privacy-minded case of only utilizing local face images with no contextual information from the original image is explored with a convolutional neural network-based classifier. Three methods of face anonymization are implemented and compared: black boxing, Gaussian blurring, and pose-tolerant face swapping. To validate these methods, a comprehensive user survey is conducted to understand the difference in viability between them. Beyond photographing, the privacy of mobile device users can sometimes be impacted in public spaces, as visual eavesdropping or “shoulder surfing” attacks on device screens become feasible. Malicious individuals can easily glean personal data from smartphone and mobile device screens while they are accessed visually. In order to protect displayed user content, anovel, sensor-based visual eavesdropping detection scheme using integrated device cameras is proposed. In order to selectively obfuscate private content while an attacker is nearby, a dynamic scheme for detecting and hiding private content is also developed utilizing User-Interface-as-an-Image (UIaaI). A deep, convolutional object detection network is trained and utilized to identify sensitive content under this scheme. To allow users to customize the types ofcontent to hide, dynamic training sample generation is introduced to retrain the content detection network with very few original UI samples. Web applications are also considered with a Chrome browser extension which automates the detection and obfuscation of sensitive web page fields through HTML parsing and CSS injection

    Animal Rights Coalitions Coordinator\u27s Report \u2786

    Get PDF
    New strategies fuel progress, promise even greater gains Milestones On a personal note Here\u27s what you can do to make a difference Promoting animal rights at the community level (Ann T. Koros) Animal care communities and Sunshine Laws Alternative testing joins the scientific mainstream (Susan Fowler) The media passes on the mesage Computers as alternatives Non-violent alternatives,--the emerging new scienc

    Online Self-Healing Control Loop to Prevent and Mitigate Faults in Scientific Workflows

    Get PDF
    Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. In spite of many success stories, a key challenge for running workflow in distributed systems is failure prediction, detection, and recovery. In this paper, we present a novel online self-healing framework, where failures are predicted before they happen, and are mitigated when possible. The proposed approach is to use control theory developed as part of autonomic computing, and in particular apply the proportional-integral-derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, to mitigate faults by adjusting the inputs of the mechanism. The PID controller aims at detecting the possibility of a fault far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of the Big Data era—data footprint and memory usage. We define, implement, and evaluate PID controllers to autonomously manage data and memory usage of a bioinformatics workflow that consumes/produces over 4.4TB of data, and requires over 24TB of memory to run all tasks concurrently. Experimental results indicate that workflow executions may significantly benefit from PID controllers, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed control loop, and faults are detected and mitigated far in advance

    Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflows

    Get PDF
    Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. Although the scientific community has addressed this challenge from both theoretical and practical approaches, failure prediction, detection, and recovery still raise many research questions. In this paper, we propose an approach inspired by the control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach is inspired on the proportional–integral–derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, where the controller will react to adjust its output to mitigate faults. PID controllers aim to detect the possibility of a non-steady state far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of large scale data-intensive workflows—data storage overload and memory overflow. We developed a simulator, which implements and evaluates simple standalone PID-inspired controllers to autonomously manage data and memory usage of a data-intensive bioinformatics workflow that consumes/produces over 4.4 TB of data, and requires over 24 TB of memory to run all tasks concurrently. Experimental results obtained via simulation indicate that workflow executions may significantly benefit from the controller-inspired approach, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed method, and faults are detected and mitigated far in advance of their occurrence

    Nuclear Power

    Get PDF
    We are fortunate to live in incredibly exciting and incredibly challenging time. Energy demands due to economic growth and increasing population must be satisfied in a sustainable manner assuring inherent safety, efficiency and no or minimized environmental impact. These considerations are among the reasons that lead to serious interest in deploying nuclear power as a sustainable energy source. At the same time, catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, design requirements and facilitated growing interests in advanced nuclear energy systems. This book is one in a series of books on nuclear power published by InTech. It consists of six major sections housing twenty chapters on topics from the key subject areas pertinent to successful development, deployment and operation of nuclear power systems worldwide. The book targets everyone as its potential readership groups - students, researchers and practitioners - who are interested to learn about nuclear power

    Nuclear Power - Deployment, Operation and Sustainability

    Get PDF
    We are fortunate to live in incredibly exciting and incredibly challenging time. Energy demands due to economic growth and increasing population must be satisfied in a sustainable manner assuring inherent safety, efficiency and no or minimized environmental impact. These considerations are among the reasons that lead to serious interest in deploying nuclear power as a sustainable energy source. At the same time, catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, design requirements and facilitated growing interests in advanced nuclear energy systems. This book is one in a series of books on nuclear power published by InTech. It consists of six major sections housing twenty chapters on topics from the key subject areas pertinent to successful development, deployment and operation of nuclear power systems worldwide. The book targets everyone as its potential readership groups - students, researchers and practitioners - who are interested to learn about nuclear power
    corecore